仿生智能优化算法及其应用研究
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摘要
科学研究和工程应用领域经常遇到优化问题，如何设计有效的模型和算法求解这些优化问题一直是一个研究的热点。如，在电子对抗研究领域，国内外协同作战研究方面存在的武器-目标分配问题，这是一个典型的组合优化问题，本文针对电子对抗中的协同干扰问题建立了适合仿生智能优化算法求解的数学模型，并重点研究了如何改进现有仿生智能优化算法求解该模型。
关于仿生智能优化算法，在理论和应用上需要进一步的研究，目前已有的IIGA等算法在背景项目的应用中仍然存在许多问题有待研究解决。其中，下面三个方面的不足最为研究人员关注：(1)算法的普适性、鲁棒性仍然有待提高。(2)算法的延展性不够，算法性能随着优化问题规模的增大而迅速降低。(3)已有算法如何应用于工程优化问题还需要进一步的研究。论文工作在分析前人已有成果的基础上，重点对(1)和(3)两个问题开展了仿生智能优化算法及其应用的研究工作。本文主要研究成果概括如下：
(1)增强型自适应进化算法
提出了增强型自适应进化算法(ESEA)。设计了贪婪繁殖算子、策略选择算子、X进化算子、种群多样性维持算子和进化策略学习算子来组成算法的进化结构；设计了多种有效的进化策略，即候选解产生策略；设计了概率选择策略并用于进化种群中的个体，采用改进的概率模型计算策略被选择的概率；引进了一种学习机制，可根据策略在进化过程中的表现自适应地学习每种策略的选择概率。通过对比实验发现提出的增强型自适应进化算法相比同类算法提高了普适性和鲁棒性，并且新的操作算子、多进化策略和自适应学习机制对算法性能的提升起重要的作用。
(2)基于自适应学习群体搜索技术的集成进化算法
针对第一算法难以适应战场态势多变、战场投入的武器和目标规模与日俱增的问题，提出了基于自适应学习群体搜索技术的集成进化算法(EEA-SLPS)。算法采用了多种群随机搜索技术和并行工作机制。与增强型自适应进化算法相比，本算法的主要特点在于集成了多种随机搜索技术，并使它们以有效的方式进化子种群。在该算法中，将整个进化群体分成三个子群体，并采用三个子算法分别对子群体进行进化。论文设计了多种不同性质的信息交流方式(IEMs)。做了大量的IEMs性能测试实验。通过对实验数据结果的分析，发现信息交流方向应由包含整个种群最优解的子群指向不包含整个种群最优解的子群，交流方向不应该是预定义的，应是动态自适应的。对比实验结果表明所设计的算法比同类算法在鲁棒性和普适性上均有所提高。
(3)求解协同干扰决策问题的启发式自适应离散差分进化算法
针对现代作战环境中多UCAV (Unmanned Combat Air Vehicle)协同对抗多部威胁雷达任务规划这一军事运筹决策问题，提出了基于多指标干扰效能综合评估方法的多UCAV协同干扰决策问题优化模型。为了有效求解多UCAV协同干扰决策问题模型，提出了启发式自适应离散差分进化算法。为了提高算法求解特定领域问题的效率，设计了基于威胁度的扩展型整数编码方案、基于威胁度的启发式个体调整操作和基于约束满足的个体修复等操作。实验结果表明提出的启发式自适应离散差分进化算法相比同类算法具有更高的鲁棒性和更好的求解效率。
(4)自适应离散差分进化算法策略选择
提出了求解协同干扰武器目标分配问题(CJWTA)的自适应离散差分进化(SaDDE)算法。好的策略池决定SaDDE算法的主要性能。论文中引入了基于相对排列顺序的标度法（RPOSM)，通过RPOSM改进了层次分析法，提出了基于RPOSM的层次分析法(RPOSM-AHP)以解决策略选择问题，通过理论和实验数据结合的方法给出了解决策略选择问题的可行方案。
Optimization problems widely exist in scientific and engineering fields. It has been a hot topicthat how to design effective models and algorithms to solve these optimization problems. Forexample, the WTA problems in the research field of ECM. It is a classical combination problem.This paper tries to construct optimization model for the WTA problem of cooperative jammingand solve it through bio-inspired intelligent optimization method.
It needs research in theory and in application about these bio-inspired intelligent optimizationalgorithms. Many problems which exist in the algorithms such as IIGA for the WTA applicationneed to be overcome. Among which, the researchers mainly pay close attention to the followingthree aspects:(1) The universality and robustness of the algorithms remain need to be enhanced.
(2) The ductility of the algorithms is insufficient, the performance decreases quickly with thedimension of the problem increases.(3) It needs further research that how to apply existingalgorithm to engineering optimization problems. Based on the analysis of previous work, wehave done some research work on the algorithms and their applications. The mainly researchresults are summarized as follows:
(1) Enhanced self-adaptive evolution algorithm
Greedy breeding operator, strategy selecting operator, X-evolution operator, populationdiversity maintaining operator, strategy learning operator are designed to consist the algorithmstructure. Besides, the algorithm uses many effective candidate solution generation strategies(CSGSes). An improved probability model is adopted to describe the probability of a strategybeing used to update an antibody. A self-adaptive learning mechanism is introduced into thealgorithm, so the selection probability of each strategy can learning from its experience ingenerating new individuals. Experimental results demonstrate that the proposed algorithm ismore effective than its competitors, and the new operators, multiple solution generatingstrategies and self-adaptive mechanism are effective for generating better solutions.
(2) Ensemble evolution algorithm with self-adaptive learning techniques
According to the battlefield situation is easily change and the scale of the weapon and thetarget increasing day by day. Thus, an ensemble evolution algorithm with self-adaptive learningtechniques is proposed. The algorithm integrates many population based stochastic searchalgorithms in parallel manner. Different from the proposed algorithm in (1), this algorithmmainly focuses on ensemble of the sub-algorithms which with self-adaptive learning mechanismand make them evolve the sub-algorithms in effective manner. In the proposed algorithm, thepopulation is divided into three sub-populations and the sub-algorithms are employed to evolvethe sub-populations in parallel manner, respectively. We have designed many differentinformation exchange manners (IEMs). Then, many experiments have been done. We have found that the information exchange direction should be from the sub-population which with the globalbest individual at current generation to the sub-population(s) without, the direction should not bepredefined and should be self-adaptive. Experimental results demonstrate that the proposedalgorithm with better performance of robustness and universality.
(3) Cooperative jamming decision making based on heuristic self-adaptive discrete differentialevolution algorithm
An optimization model of cooperative jamming decision problem is proposed based onmulti-index jamming effect comprehensive evaluation method for the military operations taskprogramming problem of multiple UCAVs (Unmanned Combat Aerial Vehicles) confrontmultiple threaten radars. In order to solve the model effectively, a Heuristic Self-adaptiveDiscrete Differential Evolution (H-SDDE) algorithm is proposed. In the algorithm, threatendegree based extensional integer coding scheme, heuristic individual adjust operator andindividual repair process are designed. The experimental results indicate that the proposedalgorithm is better than its competitors.
(4) Research on strategy selecting of self-adaptive discrete differential evolution algorithm
The Self-adaptive Discrete Differential Evolution (SaDDE) is proposed to solve theCooperative Jamming Weapon-Target Assignment (CJWTA) problem. Obviously, the strategypool plays a significant role in the SaDDE algorithm. First, the Relative Permutation Order basedScale Method (RPOSM) is introduced. The analytic hierarchy process is improved by theRPOSM. Then, the RPOSM based analytic hierarchy process (RPOSM-AHP) is proposed for thestrategy selecting problem. Finally, a feasible scheme is given to solve the strategy selectionproblem by combining the theory and experimental results.
关于仿生智能优化算法，在理论和应用上需要进一步的研究，目前已有的IIGA等算法在背景项目的应用中仍然存在许多问题有待研究解决。其中，下面三个方面的不足最为研究人员关注：(1)算法的普适性、鲁棒性仍然有待提高。(2)算法的延展性不够，算法性能随着优化问题规模的增大而迅速降低。(3)已有算法如何应用于工程优化问题还需要进一步的研究。论文工作在分析前人已有成果的基础上，重点对(1)和(3)两个问题开展了仿生智能优化算法及其应用的研究工作。本文主要研究成果概括如下：
(1)增强型自适应进化算法
提出了增强型自适应进化算法(ESEA)。设计了贪婪繁殖算子、策略选择算子、X进化算子、种群多样性维持算子和进化策略学习算子来组成算法的进化结构；设计了多种有效的进化策略，即候选解产生策略；设计了概率选择策略并用于进化种群中的个体，采用改进的概率模型计算策略被选择的概率；引进了一种学习机制，可根据策略在进化过程中的表现自适应地学习每种策略的选择概率。通过对比实验发现提出的增强型自适应进化算法相比同类算法提高了普适性和鲁棒性，并且新的操作算子、多进化策略和自适应学习机制对算法性能的提升起重要的作用。
(2)基于自适应学习群体搜索技术的集成进化算法
针对第一算法难以适应战场态势多变、战场投入的武器和目标规模与日俱增的问题，提出了基于自适应学习群体搜索技术的集成进化算法(EEA-SLPS)。算法采用了多种群随机搜索技术和并行工作机制。与增强型自适应进化算法相比，本算法的主要特点在于集成了多种随机搜索技术，并使它们以有效的方式进化子种群。在该算法中，将整个进化群体分成三个子群体，并采用三个子算法分别对子群体进行进化。论文设计了多种不同性质的信息交流方式(IEMs)。做了大量的IEMs性能测试实验。通过对实验数据结果的分析，发现信息交流方向应由包含整个种群最优解的子群指向不包含整个种群最优解的子群，交流方向不应该是预定义的，应是动态自适应的。对比实验结果表明所设计的算法比同类算法在鲁棒性和普适性上均有所提高。
(3)求解协同干扰决策问题的启发式自适应离散差分进化算法
针对现代作战环境中多UCAV (Unmanned Combat Air Vehicle)协同对抗多部威胁雷达任务规划这一军事运筹决策问题，提出了基于多指标干扰效能综合评估方法的多UCAV协同干扰决策问题优化模型。为了有效求解多UCAV协同干扰决策问题模型，提出了启发式自适应离散差分进化算法。为了提高算法求解特定领域问题的效率，设计了基于威胁度的扩展型整数编码方案、基于威胁度的启发式个体调整操作和基于约束满足的个体修复等操作。实验结果表明提出的启发式自适应离散差分进化算法相比同类算法具有更高的鲁棒性和更好的求解效率。
(4)自适应离散差分进化算法策略选择
提出了求解协同干扰武器目标分配问题(CJWTA)的自适应离散差分进化(SaDDE)算法。好的策略池决定SaDDE算法的主要性能。论文中引入了基于相对排列顺序的标度法（RPOSM)，通过RPOSM改进了层次分析法，提出了基于RPOSM的层次分析法(RPOSM-AHP)以解决策略选择问题，通过理论和实验数据结合的方法给出了解决策略选择问题的可行方案。
Optimization problems widely exist in scientific and engineering fields. It has been a hot topicthat how to design effective models and algorithms to solve these optimization problems. Forexample, the WTA problems in the research field of ECM. It is a classical combination problem.This paper tries to construct optimization model for the WTA problem of cooperative jammingand solve it through bio-inspired intelligent optimization method.
It needs research in theory and in application about these bio-inspired intelligent optimizationalgorithms. Many problems which exist in the algorithms such as IIGA for the WTA applicationneed to be overcome. Among which, the researchers mainly pay close attention to the followingthree aspects:(1) The universality and robustness of the algorithms remain need to be enhanced.
(2) The ductility of the algorithms is insufficient, the performance decreases quickly with thedimension of the problem increases.(3) It needs further research that how to apply existingalgorithm to engineering optimization problems. Based on the analysis of previous work, wehave done some research work on the algorithms and their applications. The mainly researchresults are summarized as follows:
(1) Enhanced self-adaptive evolution algorithm
Greedy breeding operator, strategy selecting operator, X-evolution operator, populationdiversity maintaining operator, strategy learning operator are designed to consist the algorithmstructure. Besides, the algorithm uses many effective candidate solution generation strategies(CSGSes). An improved probability model is adopted to describe the probability of a strategybeing used to update an antibody. A self-adaptive learning mechanism is introduced into thealgorithm, so the selection probability of each strategy can learning from its experience ingenerating new individuals. Experimental results demonstrate that the proposed algorithm ismore effective than its competitors, and the new operators, multiple solution generatingstrategies and self-adaptive mechanism are effective for generating better solutions.
(2) Ensemble evolution algorithm with self-adaptive learning techniques
According to the battlefield situation is easily change and the scale of the weapon and thetarget increasing day by day. Thus, an ensemble evolution algorithm with self-adaptive learningtechniques is proposed. The algorithm integrates many population based stochastic searchalgorithms in parallel manner. Different from the proposed algorithm in (1), this algorithmmainly focuses on ensemble of the sub-algorithms which with self-adaptive learning mechanismand make them evolve the sub-algorithms in effective manner. In the proposed algorithm, thepopulation is divided into three sub-populations and the sub-algorithms are employed to evolvethe sub-populations in parallel manner, respectively. We have designed many differentinformation exchange manners (IEMs). Then, many experiments have been done. We have found that the information exchange direction should be from the sub-population which with the globalbest individual at current generation to the sub-population(s) without, the direction should not bepredefined and should be self-adaptive. Experimental results demonstrate that the proposedalgorithm with better performance of robustness and universality.
(3) Cooperative jamming decision making based on heuristic self-adaptive discrete differentialevolution algorithm
An optimization model of cooperative jamming decision problem is proposed based onmulti-index jamming effect comprehensive evaluation method for the military operations taskprogramming problem of multiple UCAVs (Unmanned Combat Aerial Vehicles) confrontmultiple threaten radars. In order to solve the model effectively, a Heuristic Self-adaptiveDiscrete Differential Evolution (H-SDDE) algorithm is proposed. In the algorithm, threatendegree based extensional integer coding scheme, heuristic individual adjust operator andindividual repair process are designed. The experimental results indicate that the proposedalgorithm is better than its competitors.
(4) Research on strategy selecting of self-adaptive discrete differential evolution algorithm
The Self-adaptive Discrete Differential Evolution (SaDDE) is proposed to solve theCooperative Jamming Weapon-Target Assignment (CJWTA) problem. Obviously, the strategypool plays a significant role in the SaDDE algorithm. First, the Relative Permutation Order basedScale Method (RPOSM) is introduced. The analytic hierarchy process is improved by theRPOSM. Then, the RPOSM based analytic hierarchy process (RPOSM-AHP) is proposed for thestrategy selecting problem. Finally, a feasible scheme is given to solve the strategy selectionproblem by combining the theory and experimental results.
引文
[1]邢文训,谢金星.现代优化计算方法[M].2005:2-46.
[2] S. P. Lloyd, H. S. Witsenhausen. Weapons allocation is NP-complete. The1986SummerConference on Simulation[C],1986:1054-1058.
[3]范铁虎,秦贵和,赵启.用于求解连续优化问题的均匀设计和改造BLX-α的分散搜索算法.计算机研究与发展[J],2011,48(06):1049-1058.
[4]钟伟才,刘静,刘芳,焦李成.组合优化多智能体进化算法.计算机学报[J],2004,27(10):1341-1354.
[5] W. D. Song, C. W. Zhao, J. X. Huo. Improved differential evolution algorithm for solvingWTA problem. International Conference on Energy and Environmental Science[C],2011:1348-1353.
[6] L. Wang, G. Zhou, Y. Xu, M. Liu. An enhanced Pareto-based artificial bee colony algorithmfor the multi-objective flexible job-shop scheduling. International Journal of AdvancedManufacturing Technology[J],2012,60(09):1111-1123.
[7] D. Martens, B. Baesens, T. Fawcett. Editorial survey: swarm intelligence for data mining.Machine Learning[J],2011,82(1):1-42.
[8] B. Alatas, E. Akin, A. Karci. Modenar: Multi-objective differential evolution algorithm formining numeric association rules. Applied Soft Computing[J],2008,8(1):646-656.
[9] T. Y. Wu, G. H. Liaw, S. W. Huang, W. T. Lee, C. C. Wu. A GA-based mobile RFIDlocalization scheme for internet of things. Personal and Ubiquitous Computing[J],2012,16(3):245-258.
[10] S. Pandey, L. L. Wu, S. M. Guru, R. Buyya. A particle swarm optimization-based heuristicfor scheduling workflow applications in cloud computing environments. The24th IEEEInternational Conference on Advanced Information Networking and Applications[C],2011:400-407.
[11] S. B. Zhong, Z. S. He. The scheduling algorithm of grid task based on PSO and cloud model.Advanced Measurement and Test, Parts1and2[C],2010:1487-1492.
[12] P. Y. Yin. A discrete particle swarm algorithm for optimal polygonal approximation of digitalcurves. Journal of Visual Communication and Image Representation[J],2004,15(2):241-260.
[13] J. F. Connolly, E. Granger, R. Sabourin. Evolution of heterogeneous ensembles throughdynamic particle swarm optimization for video-based face recognition. PatternRecognition[J],2012,45(7):2460-2477.
[14] O. Gusikhin, D. Filev, N. Rychtyckyj. Intelligent vehicle systems-applications and newtrends. Proceedings of the Third International Conference on Informatics in Control,Automation and Robotics: Robotics and Automation[C],2006:IS11-IS18.
[15] Q. L. Wei, H. G. Zhang, J. Dai. Model-free multiobjective approximate dynamicprogramming for discrete-time nonlinear systems with general performance index functions.Neurocomputing[J],2009,72(07):1839-1848.
[16] H. B. He, A. Kusiak. Special issue on computational intelligence in smart grid. IEEEComputational Intelligence Magazine[J],2011,6(3):12-64.
[17] C. Lv, M. D. Ma, H. Li, J. F. Ma, B. Niu. Security enhancement of thecommunication-efficient AUTHMAC_DH protocols. Security and CommunicationNetworks[J],2012,5(6):674-680.
[18] D. Dasgupta. Special issue on computational intelligence in cyber security-Preface. IEEEComputational Intelligence Magazine[J],2008,3(2):21-21.
[19]刘晓平,何涛,黄永红,唐卫清,刘慎权.基于优先表的工程约束求解算法研究.软件学报[J],1999,10(11):1127-1131.
[20]傅英定,成孝予,唐应辉.最优化理论与方法[M].2007:120-247.
[21]王玲,薄列峰,刘芳,焦李成.最小二乘隐空间支持向量机.计算机学报[J],2005,28(08):1302-1307.
[22] L. M. Rios, N. V. Sahinidis. Derivative-free optimization: A review of algorithms andcomparison of software implementations. Journal of Global Optimization[J],2011,12(12):1-47.
[23] D. E. Goldberg. Genetic algorithms in search, optimization, and machine learning[M].1989:45-105.
[24] S. Kirkpatrick, C. D. Gelatt Jr, M. P. Vecchi. Optimization by simulated annealing.Science[J],1983,220(4598):671-680.
[25] F. Glover. Tabu search: a tutorial. Interfaces[J],1990,20(4):74-94.
[26] X. Yao, Y. Liu, G. M. Lin. Evolutionary programming made faster. IEEE Transactions onEvolutionary Computation[J],1999,3(2):82-102.
[27] K. F. Liem. Evolutionary strategies and morphological innovations: cichlid pharyngeal jaws.Systematic Biology[J],1973,22(4):425-441.
[28] R. R. Barton, J. S. Ivey Jr. Nelder-mead simplex modifications for simulation optimization.Management Science[J],1996,34(65):954-973.
[29] M. Dorigo, L. M. Gambardella. Ant colony system: a cooperative learning approach to thetraveling salesman problem. IEEE Transactions on Evolutionary Computation[J],1997,31(11):53-66.
[30] J. Kennedy, R. Eberhart. Particle swarm optimization. IEEE International Conference onNeural Networks[C],1995:1942-1948.
[31] U. Aickelin, J. Greensmith, J. Twycross. Immune system approaches to intrusion detection–areview. Artificial Immune Systems[J],2004,316-329.
[32] R. Storn, K. Price. Differential evolution–a simple and efficient heuristic for globaloptimization over continuous spaces. Journal of Global Optimization[J],1997,11(4):341-359.
[33] D. Karaboga, An idea based on honey bee swarm for numerical optimization[R],2005.
[34] J. D. Knowles, D. W. Corne. M-PAES: a memetic algorithm for multiobjective optimization.IEEE Congress on Evolutionary Computation[C],2000:325-332.
[35]董文永,张文生,于瑞国.求解组合优化问题伊藤算法的收敛性和期望收敛速度分析.计算机学报[J],2011,34(4):636-646.
[36] H. Kubiny. Variable selection in QSAR studies. I. An evolutionary algorithm. QuantitativeStructure‐Activity Relationships[J],1994,13(3):285-294.
[37]王瑜.基于多方法融合的进化算法研究,[博士学位论文].中国科学技术大学.2011.
[38] C. A. Mendez, J. Cerda, I. E. Grossmann, I. Harjunkoski, M. Fahl. State-of-the-art review ofoptimization methods for short-term scheduling of batch processes. Computers&ChemicalEngineering[J],2006,30(06):913-946.
[39] N. V. Sahinidis. Optimization under uncertainty: state-of-the-art and opportunities.Computers&Chemical Engineering[J],2004,28(06):971-983.
[40] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan. A fast and elitist multiobjective geneticalgorithm: NSGA-II. IEEE Transactions on Evolutionary Computation[J],2002,6(2):182-197.
[41] C. M. Fonseca, P. J. Fleming. Multiobjective optimization and multiple constraint handlingwith evolutionary algorithms-Part I: A unified formulation. IEEE Transactions on SystemsMan and Cybernetics Part a-Systems and Humans[J],1998,28(1):26-37.
[42] E. Zitzler, K. Deb, L. Thiele. Comparison of multiobjective evolutionary algorithms:empirical results. Evolutionary Computation[J],2000,8(2):173-195.
[43] E. Zitzler, L. Thiele. Multiobjective evolutionary algorithms: A comparative case study andthe strength pareto approach. IEEE Transactions on Evolutionary Computation[J],1999,3(4):257-271.
[44] E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, V. G. da Fonseca. Performanceassessment of multiobjective optimizers: An analysis and review. IEEE Transactions onEvolutionary Computation[J],2003,7(2):117-132.
[45] J. H. Holland. Adaptation in natural and artificial systems[M].1975:37-69.
[46] F. Glover. Tabu search-part I. ORSA Journal on computing[J],1989,1(3):190-206.
[47] R. Eberhart, J. Kennedy. A new optimizer using particle swarm theory. Proceeding of theSixth International Symposium on Micro Machine and Human Science[C],1995:39-43.
[48] M. Dorigo, M. Birattari, T. Stutzle. Ant colony optimization. IEEE ComputationalIntelligence Magazine[J],2006,1(4):28-39.
[49] L. N. De Castro, F. J. Von Zuben. Learning and optimization using the clonal selectionprinciple. IEEE Transactions on Evolutionary Computation[J],2002,6(3):239-251.
[50] R. Storn, K. Price. Differential evolution-a simple and efficient adaptive scheme for globaloptimization over continuous spaces. International computer scienceinstitute-publications-TR[C],1995:1-12.
[51] A. K. Qin, V. L. Huang, P. N. Suganthan. Differential Evolution Algorithm With StrategyAdaptation for Global Numerical Optimization. Ieee Transactions on EvolutionaryComputation[J],2009,13(2):398-417.
[52]郭鹏.差分进化算法改进研究,[博士学位论文].天津大学.2012.
[53] J. Chen, B. Xin, Z. Peng, F. Pan. Statistical learning makes the hybridization of particleswarm and differential evolution more efficient—a novel hybrid optimizer. Science in ChinaSeries F: Information Sciences[J],2009,52(7):1278-1282.
[54] S. Das, S. Dasgupta, A. Biswas, A. Abraham. Automatic circle detection on images withannealed differential evolution. Eighth International Conference on Hybrid IntelligentSystems[C],2008:684-689.
[55] W. Gong, Z. Cai, C. X. Ling. DE/BBO: a hybrid differential evolution withbiogeography-based optimization for global numerical optimization. Soft Computing[J],2010,15(4):645-665.
[56] S. Rahnamayan, H. R. Tizhoosh, M. M. Salama. Opposition-based differential evolution.IEEE Transactions on Evolutionary Computation[J],2008,12(1):64-79.
[57] J. Teo. Exploring dynamic self-adaptive populations in differential evolution. SoftComputing[J],2006,10(8):673-686.
[58] M. Ali, M. Pant, V. Singh. Two modified differential evolution algorithms and theirapplications to engineering design problems. World Journal of Modelling and Simulation[J],2010,6(1):72-80.
[59] S. Das, A. Abraham, U. K. Chakraborty, A. Konar. Differential Evolution Using aNeighborhood-Based Mutation Operator. IEEE Transactions on EvolutionaryComputation[J],2009,13(3):526-553.
[60] J. Brest, S. Greiner, B. Boskovic, M. Mernik, V. Zumer. Self-adapting control parameters indifferential evolution: a comparative study on numerical benchmark problems. IEEETransactions on Evolutionary Computation[J],2006,10(6):646-657.
[61]常彦鑫,高正红.自适应差分进化算法在气动优化设计中的应用.航空学报[J],2009,30(09):1590-1596.
[62]陈秋霞,俞立,黄骅.基于RVM的非线性预测控制及在聚丙烯牌号切换中的应用.控制与决策[J],2010,25(08):1241-1245.
[63]吴斌,蔡红,樊树海,蒋南云.双倍体差分进化粒子群算法在VRPSDP中的应用研究.系统工程理论与实践[J],2010,30(03):520-526.
[64]徐新黎,施莉娜,范丽霞,王万良.基于广义粗糙集的流程工业车间调度研究.控制与决策[J],2012,27(01):8-14.
[65] R. Zhang, S. J. Song, C. Wu. A hybrid differential evolution algorithm for job shopscheduling problems with expected total tardiness criterion. Applied Soft Computing[J],2013,13(3):1448-1458.
[66] G. X. Zhang, J. X. Cheng, M. Gheorghe, Q. Meng. A hybrid approach based on differentialevolution and tissue membrane systems for solving constrained manufacturing parameteroptimization problems. Applied Soft Computing[J],2013,13(3):1528-1542.
[67] A. Ponsich, C. A. C. Coello. A hybrid differential evolution-tabu search algorithm for thesolution of job-shop scheduling problems. Applied Soft Computing[J],2013,13(1):462-474.
[68] X. T. Li, M. H. Yin. An opposition-based differential evolution algorithm for permutationflow shop scheduling based on diversity measure. Advances in Engineering Software[J],2013,55(01):10-31.
[69] G. L. Deng, X. S. Gu. A hybrid discrete differential evolution algorithm for the no-idlepermutation flow shop scheduling problem with makespan criterion. Computers&Operations Research[J],2012,39(9):2152-2160.
[70] D. Davendra, I. Zelinka, M. Bialic-Davendra, R. Senkerik, R. Jasek. Clustered enhanceddifferential evolution for the blocking flow shop scheduling problem. Central EuropeanJournal of Operations Research[J],2012,20(4):679-717.
[71] A. R. Yildiz. A new hybrid differential evolution algorithm for the selection of optimalmachining parameters in milling operations. Applied Soft Computing[J],2013,13(3):1561-1566.
[72] P. Mesejo, R. Ugolotti, F. Di Cunto, M. Giacobini, S. Cagnoni. Automatic hippocampuslocalization in histological images using Differential Evolution-based deformable models.Pattern Recognition Letters[J],2013,34(3):299-307.
[73] H. Kazemipoor, R. Tavakkoli-Moghaddam, P. Shahnazari-Shahrezaei, A. Azaron. Adifferential evolution algorithm to solve multi-skilled project portfolio scheduling problems.International Journal of Advanced Manufacturing Technology[J],2013,64(5-8):1099-1111.
[74]陈霄. DNA遗传算法及应用研究,[博士学位论文].浙江大学.2010.
[75] C. Garcia-Martinez, M. Lozano, F. Herrera, D. Molina, A. Sánchez. Global and localreal-coded genetic algorithms based on parent-centric crossover operators. European Journalof Operational Research[J],2008,185(3):1088-1113.
[76] K. Deep, M. Thakur. A new crossover operator for real coded genetic algorithms. AppliedMathematics and Computation[J],2007,188(1):895-911.
[77] K. Deep, M. Thakur. A new mutation operator for real coded genetic algorithms. AppliedMathematics and Computation[J],2007,193(1):211-230.
[78] Q. Zhang, J. Sun, E. Tsang. An evolutionary algorithm with guided mutation for themaximum clique problem. IEEE Transactions on Evolutionary Computation,[J],2005,9(2):192-200.
[79] P. Chootinan, A. Chen. Constraint handling in genetic algorithms using a gradient-basedrepair method. Computers&Operations Research[J],2006,33(8):2263-2281.
[80] S. Venkatraman, G. G. Yen. A generic framework for constrained optimization using geneticalgorithms. IEEE Transactions on Evolutionary Computation[J],2005,9(4):424-435.
[81] Y. Wang, Z. Cai, G. Guo, Y. Zhou. Multiobjective optimization and hybrid evolutionaryalgorithm to solve constrained optimization problems. IEEE Transactions on Systems, Man,and Cybernetics, Part B: Cybernetics[J],2007,37(3):560-575.
[82] C. M. Fonseca, P. J. Fleming. Genetic algorithms for multiobjective optimization:Formulation, discussion and generalization. Proceedings of the fifth international conferenceon genetic algorithms[C],1993:416-423.
[83] N. Srinivas, K. Deb. Muiltiobjective optimization using nondominated sorting in geneticalgorithms. Evolutionary Computation[J],1994,2(3):221-248.
[84] J. Horn, N. Nafpliotis, D. E. Goldberg. A niched Pareto genetic algorithm for multiobjectiveoptimization. Proceedings of the First IEEE Conference on Evolutionary Computation[C],1994:82-87.
[85] E. Zitzler, M. Laumanns, L. Thiele, E. Zitzler, E. Zitzler, L. Thiele, and L. Thiele,"SPEA2:Improving the strength Pareto evolutionary algorithm," ed: Eidgen ssische TechnischeHochschule Zürich (ETH), Institut für Technische Informatik und Kommunikationsnetze(TIK),2001.
[86] J. D. Knowles, D. W. Corne. Approximating the nondominated front using the Paretoarchived evolution strategy. Evolutionary Computation[J],2000,8(2):149-172.
[87] D. W. Corne, J. D. Knowles, M. J. Oates. The Pareto envelope-based selection algorithm formultiobjective optimization. Parallel Problem Solving from Nature PPSN VI[C],2000:839-848.
[88] D. W. Corne, N. R. Jerram, J. D. Knowles, M. J. Oates. PESA-II: Region-based selection inevolutionary multiobjective optimization. Proceedings of the Genetic and EvolutionaryComputation Conference[C],2001:1-8.
[89] M. Erickson, A. Mayer, J. Horn. The niched pareto genetic algorithm applied to the designof groundwater remediation systems. Evolutionary Multi-Criterion Optimization[C],2001:681-695.
[90] K. Deb, S. Agrawal, A. Pratap, T. Meyarivan. A fast elitist non-dominated sorting geneticalgorithm for multi-objective optimization: NSGA-II. Lecture Notes in Computer Science[J],2000,1917(21):849-858.
[91] B. Mansoornejad, N. Mostoufi, F. Jalali-Farahani. A hybrid GA–SQP optimization techniquefor determination of kinetic parameters of hydrogenation reactions. Computers&ChemicalEngineering[J],2008,32(7):1447-1455.
[92] X. Luo, Q.-Y. Wen, G. Fieg. A hybrid genetic algorithm for synthesis of heat exchangernetworks. Computers&Chemical Engineering[J],2009,33(6):1169-1181.
[93] H. Chen, N. S. Flann, D. W. Watson. Parallel genetic simulated annealing: a massivelyparallel SIMD algorithm. IEEE Transactions on Parallel and Distributed Systems[J],1998,9(2):126-136.
[94] Y.-T. Kao, E. Zahara. A hybrid genetic algorithm and particle swarm optimization formultimodal functions. Applied Soft Computing[J],2008,8(2):849-857.
[95] B.-B. Li, L. Wang. A hybrid quantum-inspired genetic algorithm for multiobjective flowshop scheduling. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics[J],2007,37(3):576-591.
[96] X. H. Shi, Y. C. Liang, H. P. Lee, C. Lu, L. M. Wang. An improved GA and a novelPSO-GA-based hybrid algorithm. Information Processing Letters[J],2005,93(5):255-261.
[97]冯晓强,宋笔锋,李占科,桑建华.超声速飞机低声爆布局混合优化方法研究.航空学报[J],2012,33(01):1+10-11.
[98]王伟,赵俊杰,王辉.基于混合算法的MIMO雷达正交多相码设计.系统工程与电子技术[J],2013,35(02):294-298.
[99]司维超,韩维,史玮韦.基于HPSO算法和GA的舰载机甲板布放方法比较.系统工程与电子技术[J],2013,35(02):338-344.
[100] J. Torres, J. L. Guardado, F. Rivas-Davalos, S. Maximov, E. Melgoza. A genetic algorithmbased on the edge window decoder technique to optimize power distribution systemsreconfiguration. International Journal of Electrical Power&Energy Systems[J],2013,45(1):28-34.
[101] M. Mehrjoo, N. Khaji, M. Ghafory-Ashtiany. Application of genetic algorithm in crackdetection of beam-like structures using a new cracked Euler-Bernoulli beam element.Applied Soft Computing[J],2013,13(2):867-880.
[102] R. Koker. A genetic algorithm approach to a neural-network-based inverse kinematicssolution of robotic manipulators based on error minimization. Information Sciences[J],2013,222(01):528-543.
[103] D. S. Jung, C. Y. Kim. Finite element model updating on small-scale bridge model usingthe hybrid genetic algorithm. Structure and Infrastructure Engineering[J],2013,9(5):481-495.
[104] L. De Giovanni, G. Massi, F. Pezzella. An adaptive genetic algorithm for large-size openstack problems. International Journal of Production Research[J],2013,51(3):682-697.
[105] A. Brunetti. A fast fine-grained genetic algorithm for spectrum fitting: An application toX-ray spectra. Computer Physics Communications[J],2013,184(3):573-578.
[106] M. Dorigo, V. Maniezzo, A. Colorni, The ant system: an autocatalytic optimizingprocess[R],1991.
[107]葛连升.基于蚁群优化的组播路由算法研究,[博士学位论文].山东大学.2010.
[108] M. Dorigo, L. M. Gambardella. Ant colony system: A cooperative learning approach to thetraveling salesman problem. IEEE Transactions on Evolutionary Computation,[J],1997,31(11):53-66.
[109]陈崚,沈洁,秦玲,陈宏建.基于分布均匀度的自适应蚁群算法.软件学报[J],2003,14(08):1379-1387.
[110]黄国锐,曹先彬,王煦法.基于信息素扩散的蚁群算法.电子学报[J],2004,35(05):865-868.
[111]柯良军,冯祖仁,冯远静.有限级信息素蚁群算法.自动化学报[J],2006,32(02):296-303.
[112] F. Abbattista, N. Abbattista, L. Caponetti. An evolutionary and cooperative agents modelfor optimization. IEEE International Conference on Evolutionary Computation[C],1995:668-671.
[113]丁建立,陈增强,袁著祉.遗传算法与蚂蚁算法的融合.计算机研究与发展[J],2003,40(09):1351-1356.
[114] Z. J. Lee, C. Y. Lee. An immunity-based ant colony optimization algorithm for solvingweapon–target assignment problem. Applied Soft Computing[J],2002,02(02):39-47.
[115]姚锋,邢立宁.求解卫星地面站调度问题的演化学习型蚁群算法.系统工程与电子技术[J],2012,34(11):2270-2274.
[116]宋超,刘明,龚海刚,陈贵海,王晓敏.基于蚁群优化解决传感器网络中的能量洞问题.软件学报[J],2009,20(10):2729-2743.
[117]段海滨,丁全心,常俊杰,刘森琪.基于并行蚁群优化的多UCAV任务分配仿真平台.航空学报[J],2008,29(S1):192-197.
[118]马冠军,段海滨,刘森琪,于亚翔.基于MAX-MIN自适应蚁群优化的无人作战飞机航路规划.航空学报[J],2008,29(S1):243-248.
[119] C. J. Ting, C. H. Chen. A multiple ant colony optimization algorithm for the capacitatedlocation routing problem. International Journal of Production Economics[J],2013,141(1):34-44.
[120] E. Resendiz, L. A. Moncayo-Martinez, G. Solis. Binary ant colony optimization applied tovariable screening in the Mahalanobis-Taguchi System. Expert Systems with Applications[J],2013,40(2):634-637.
[121] X. J. Liu, H. Yi, Z. H. Ni. Application of ant colony optimization algorithm in processplanning optimization. Journal of Intelligent Manufacturing[J],2013,24(1):1-13.
[122] B. Y. Cheng, Q. Wang, S. L. Yang, X. X. Hu. An improved ant colony optimization forscheduling identical parallel batching machines with arbitrary job sizes. Applied SoftComputing[J],2013,13(2):765-772.
[123] J. Bai, G. K. Yang, Y. W. Chen, L. S. Hu, C. C. Pan. A model induced max-min ant colonyoptimization for asymmetric traveling salesman problem. Applied Soft Computing[J],2013,13(3):1365-1375.
[124]王宇嘉.多目标粒子群优化算法的全局搜索策略研究,[博士学位论文].上海交通大学.2008.
[125] J. Kennedy, R. Mendes. Population structure and particle swarm performance. Proceedingof the2002congress on evolutionary computation[C],2002:1671-1676.
[126] P. W. Moore, G. K. Venayagamoorthy. Evolving digital circuits using hybrid particle swarmoptimization and differential evolution. International Journal of Neural Systems[J],2006,16(3):163-178.
[127] R. Xu, G. K. Venayagamoorthy, D. C. Wunsch II. Modeling of gene regulatory networkswith hybrid differential evolution and particle swarm optimization. Neural Networks[J],2007,20(8):917-927.
[128] A. Chatterjee, P. Siarry. Nonlinear inertia weight variation for dynamic adaptation inparticle swarm optimization. Computers&Operations Research[J],2006,33(3):859-871.
[129] M. S. Arumugam, M. Rao. On the improved performances of the particle swarmoptimization algorithms with adaptive parameters, cross-over operators and root mean square(RMS) variants for computing optimal control of a class of hybrid systems. Applied SoftComputing[J],2008,8(1):324-336.
[130] X. Yang, J. Yuan, J. Yuan, H. Mao. A modified particle swarm optimizer with dynamicadaptation. Applied Mathematics and Computation[J],2007,189(2):1205-1213.
[131] P. Bajpai, S. Singh. Fuzzy adaptive particle swarm optimization for bidding strategy inuniform price spot market. IEEE Transactions on Power Systems[J],2007,22(4):2152-2160.
[132] A. Ratnaweera, S. K. Halgamuge, H. C. Watson. Self-organizing hierarchical particleswarm optimizer with time-varying acceleration coefficients. IEEE Transactions onEvolutionary Computation[J],2004,8(3):240-255.
[133] I. C. Trelea. The particle swarm optimization algorithm: convergence analysis andparameter selection. Information Processing Letters[J],2003,85(6):317-325.
[134]F. van den Bergh, A. P. Engelbrecht. A cooperative approach to particle swarm optimization.IEEE Transactions on Evolutionary Computation[J],2004,8(3):225-239.
[135] C. F. Juang. A hybrid of genetic algorithm and particle swarm optimization for recurrentnetwork design. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics[J],2004,34(2):997-1006.
[136]黄江涛,高正红,白俊强,赵轲,李静,许放.基于任意空间属性FFD技术的融合式翼稍小翼稳健型气动优化设计.航空学报[J],2013,34(01):37-45.
[137]王连锋,宋建社,王正元,曹继平.带硬时间窗的战场物资配送车辆路径优化.系统工程与电子技术[J],2013,35(01):1-10.
[138]刘晓,刘忠,侯文姝,许江湖.火力分配多目标规划模型的改进MOPSO算法.系统工程与电子技术[J],2013,35(02):326-330.
[139]刘钦,刘峥,刘韵佛,谢荣.多传感器优化部署下的机动目标协同跟踪算法.系统工程与电子技术[J],2013,35(02):304-309.
[140]郭霖瀚,卞洁辉,王乃超,康锐.基于PSO的方案阶段修理级别优化方法.系统工程与电子技术[J],2013,35(01):97-101.
[141] A. A. Altun, M. A. Sahman. Cost optimization of mixed feeds with the particle swarmoptimization method. Neural Computing&Applications[J],2013,22(2):383-390.
[142] Q. Feng, J. Zhang, X. Zhang, A. Hu. Optimizing well placement in a coalbed methanereservoir using the particle swarm optimization algorithm. International Journal of CoalGeology[J],2012,104(07):34-45.
[143] B. T. Jiang, F. Y. Zhao. Particle swarm optimization-based least squares support vectorregression for critical heat flux prediction. Annals of Nuclear Energy[J],2013,53(03):69-81.
[144] M. A. Mohandes. Modeling global solar radiation using Particle Swarm Optimization(PSO). Solar Energy[J],2012,86(11):3137-3145.
[145] N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, E. Teller. Equation ofstate calculations by fast computing machines. The Journal of Chemical Physics[J],1953,21(1):1087-1092.
[146]赵松原.模拟退火结合正交分解算法的气动外形最优化设计,[博士学位论文].南京航空航天大学.2006.
[147]顾元宪,项宝卫,赵国忠.一种改进的连续变量全局优化模拟退火算法.系统工程理论与实践[J],2005,04(04):103-109.
[148]徐雷.一种改进的模拟退火组合优化法.信息与控制[J],1990,01(03):1-7.
[149]巩敦卫,曾现峰,张勇.基于改进模拟退火算法的机器人全局路径规划.系统仿真学报[J],2013,25(03):480-483+488.
[150]李凯,左春荣.基于模拟退火的多处理机调度优化算法研究.系统工程与电子技术[J],2009,31(12):2963-2967.
[151]李文,陈英武,李菊芳,姚锋.基于快速模拟退火的遥感数据处理调度方法.系统工程与电子技术[J],2011,33(02):334-338.
[152]梁毓明,徐立鸿.基于改进模拟退火混合算法的移动机器人全局路径规划.控制与决策[J],2010,25(02):237-240+245.
[153]刘斌,姜囡,井元伟.基于模拟退火算法的电力系统输电阻塞成本控制.控制与决策[J],2010,25(08):1184-1188.
[154]刘以安,倪天权,张秀辉,李游.模拟退火算法在雷达干扰资源优化分配中的应用.系统工程与电子技术[J],2009,31(08):1914-1917.
[155] A. Ekbal, S. Saha. Combining feature selection and classifier ensemble using amultiobjective simulated annealing approach: application to named entity recognition. SoftComputing[J],2013,17(1):1-16.
[156] S. S. Mohammadi, A. Hezarkhani, A. E. Tercan. Optimally locating additional drill holes inthree dimensions using grade and simulated annealing. Journal of the Geological Society ofIndia[J],2012,80(5):700-706.
[157] A. Roshani, M. Salehi, A. Esfandyari. A simulated annealing algorithm for multi-mannedassembly line balancing problem. Journal of Manufacturing Systems[J],2013,32(1):238-247.
[158] D. Karaboga, B. Gorkemli, C. Ozturk, N. Karaboga. A comprehensive survey: artificial beecolony (ABC) algorithm and applications. Artificial Intelligence Review[J],2012,1-37.
[159] B. Akay, D. Karaboga. Parameter tuning for the artificial bee colony algorithm[M].2009:608-619.
[160] L. Bao, J.-c. Zeng. Comparison and analysis of the selection mechanism in the artificial beecolony algorithm. Ninth International Conference on Hybrid Intelligent Systems[C],2009:411-416.
[161] A. Aderhold, K. Diwold, A. Scheidler, M. Middendorf. Artificial bee colony optimization:A new selection scheme and its performance[M].2010:283-294.
[162] P. Guo, W. Cheng, J. Liang. Global artificial bee colony search algorithm for numericalfunction optimization. Seventh International Conference on Natural Computation[C],2011:1280-1283.
[163] A. Rajasekhar, A. Abraham, M. Pant. Levy mutated artificial bee colony algorithm forglobal optimization. IEEE International Conference on Systems, Man, and Cybernetics[C],2011:655-662.
[164] W. P. zou, Y. L. Zhu, H. N. Chen, T. Ku. Clustering approach based on von neumanntopology artificial bee colony algorithm. LNCS: Advances in Soft Computing: Foundationsof Fuzzy Logic and Soft Computing[J],2011,4529(02):789-798.
[165]雷秀娟,田建芳.蛋白质相互作用网络的蜂群信息流聚类模型与算法.计算机学报[J],2012,35(01):134-145.
[166]张银雪,田学民,邓晓刚.基于改进人工蜂群算法的盲源分离方法.电子学报[J],2012,40(10):2026-2030.
[167]李鑫滨,刘磊,马锴.基于离散人工蜂群算法的认知无线电频谱分配.系统工程与电子技术[J],2012,34(10):2136-2141.
[168] R. Zhang, S. J. Song, C. Wu. A hybrid artificial bee colony algorithm for the job shopscheduling problem. International Journal of Production Economics[J],2013,141(1):167-178.
[169] A. R. Yildiz. Optimization of cutting parameters in multi-pass turning using artificial beecolony-based approach. Information Sciences[J],2013,220(22):399-407.
[170] A. Rubio-Largo, M. A. Vega-Rodriguez, J. A. Gomez-Pulido, J. M. Sanchez-Perez. Amultiobjective approach based on artificial bee colony for the static routing and wavelengthassignment problem. Soft Computing[J],2013,17(2):199-211.
[171] A. K. Qin, P. N. Suganthan. Self-adaptive differential evolution algorithm for numericaloptimization. IEEE Congress on Evolutionary Computation[C],2005:1785-1791.
[172] R. Mallipeddi, P. N. Suganthan, Q. K. Pan, M. F. Tasgetiren. Differential evolutionalgorithm with ensemble of parameters and mutation strategies. Applied Soft Computing[J],2011,11(2):1679-1696.
[173] Q. K. Pan, P. N. Suganthan, L. Wang, L. Gao, R. Mallipeddi. A differential evolutionalgorithm with self-adapting strategy and control parameters. Computers&OperationsResearch[J],2011,38(1):394-408.
[174] W. Y. Gong, Z. H. Cai, C. X. Ling, H. Li. Enhanced differential evolution with adaptivestrategies for numerical optimization. IEEE Transactions on Systems Man and CyberneticsPart B-Cybernetics[J],2011,41(2):397-413.
[175] Y. Wang, Z. X. Cai, Q. F. Zhang. Differential evolution with composite trial vectorgeneration strategies and control parameters. IEEE Transactions on EvolutionaryComputation[J],2011,15(1):55-66.
[176] Z. J. Lee, S. F. Su, C. Y. Lee. Efficiently solving general weapon-target assignment problemby genetic algorithms with greedy eugenics. IEEE Transactions on Systems Man andCybernetics Part B-Cybernetics[J],2003,33(1):113-121.
[177] B. Xin, J. Chen, J. A. Zhang, L. H. Dou, Z. H. Peng. Efficient decision makings fordynamic weapon-target assignment by virtual permutation and tabu search heuristics. IEEETransactions on Systems Man and Cybernetics Part C-Applications and Reviews[J],2010,40(6):649-662.
[178] Z. J. Lee, W. L. Lee. A hybrid search algorithm of ant colony optimization and geneticalgorithm applied to Weapon-Target Assignment problems[M].2003:278-285.
[179] R. K. Ahuja, A. Kumar, K. C. Jha, J. B. Orlin. Exact and heuristic algorithms for theweapon-target assignment problem. Operations Research[J],2007,55(6):1136-1146.
[180] M. Clerc, J. Kennedy. The particle swarm-explosion, stability, and convergence in amultidimensional complex space. IEEE Transactions on Evolutionary Computation[J],2002,6(1):58-73.
[181]R. Mendes, J. Kennedy, J. Neves. The fully informed particle swarm: simpler, maybe better.IEEE Transactions on Evolutionary Computation[J],2004,8(3):204-210.
[182] J. J. Liang, A. K. Qin, P. N. Suganthan, S. Baskar. Comprehensive learning particle swarmoptimizer for global optimization of multimodal functions. IEEE Transactions onEvolutionary Computation[J],2006,10(3):281-295.
[183] T. Fukuda, K. Mori, M. Tsukiyama. Immune networks using genetic algorithm for adaptiveproduction scheduling.15th IFAC world congress[C],1993:57-60.
[184] V. Cutello, G. Narzisi, G. Nicosia, M. Pavone. Clonal selection algorithms: a comparativecase study using effective mutation potentials. Lecture Notes in Computer Science[J],2005,3627(1):13-28.
[185] R. Mallipeddi, P. Suganthan. Differential evolution algorithm with ensemble of populationsfor global numerical optimization. OPSEARCH[J],2009,46(2):184-213.
[186] F. Neri, V. Tirronen. Recent advances in differential evolution: a survey and experimentalanalysis. Artificial Intelligence Review[J],2010,33(1-2):61-106.
[187] N. Noman, H. Iba. Accelerating differential evolution using an adaptive local search. IEEETransactions on Evolutionary Computation[J],2008,12(1):107-125.
[188] V. Tirronen, F. Neri, T. Rossi. Enhancing differential evolution frameworks by scale factorlocal search-Part I. IEEE Congress on Evolutionary Computation[C],2009:94-101.
[189] S. Rahnamayan, H. R. Tizhoosh, M. M. A. Salama. Opposition-based differential evolution.IEEE Transactions on Evolutionary Computation[J],2008,12(1):64-79.
[190] R. Mallipeddi, S. Mallipeddi, P. Suganthan. Ensemble strategies with adaptive evolutionaryprogramming. Information Sciences[J],2010,180(9):1571-1581.
[191] J. A. Vrugt, B. A. Robinson, J. M. Hyman. Self-adaptive multimethod search for globaloptimization in real-parameter spaces. IEEE Transactions on Evolutionary Computation[J],2009,13(2):243-259.
[192] J. A. Vrugt, B. A. Robinson. Improved evolutionary optimization from genetically adaptivemultimethod search. National Academy of Sciences of the United States of America[C],2007:708-711.
[193] Y. Wang, B. Li, T. Weise, J. Y. Wang, B. Yuan, Q. J. Tian. Self-adaptive learning basedparticle swarm optimization. Information Sciences[J],2011,181(20):4515-4538.
[194] B. Xu, Y. Zhuang, Y. Xue, Z. Wang. Self-adaptive learning based immune algorithm.Journal of Central South University[J],2012,19(4):1021-1031.
[195] J. M. Baldwin. A new factor in evolution. The American Naturalist[J],1896,30(355):536-553.
[196] M. G. Gong, L. C. Jiao, L. N. Zhang. Baldwinian learning in clonal selection algorithm foroptimization. Information Sciences[J],2010,180(8):1218-1236.
[197] V. Cutello, G. Nicosia, M. Pavone. Exploring the capability of immune algorithms: acharacterization of hypermutation operators[M].2004:263-276.
[198] P. A. D. De Castro, F. J. Von Zuben. BAIS: A bayesian artificial immune system for theeffective handling of building blocks. Information Sciences[J],2009,179(10):1426-1440.
[199] M. Gong, L. Jiao, H. Du, L. Bo. Multiobjective immune algorithm with nondominatedneighbor-based selection. Evolutionary Computation[J],2008,16(2):225-255.
[200] M. G. Gong, L. C. Jiao, X. R. Zhang. A population-based artificial immune system fornumerical optimization. Neurocomputing[J],2008,72(1-3):149-161.
[201] L. C. Jiao, Y. Y. Li, M. G. Gong, X. R. Zhang. Quantum-inspired immune clonal algorithmfor global optimization. IEEE Transactions on Systems Man and Cybernetics PartB-Cybernetics[J],2008,38(5):1234-1253.
[202] G. C. Luh, C. H. Chueh. A multi-modal immune algorithm for the job-shop schedulingproblem. Information Sciences[J],2009,179(10):1516-1532.
[203] J. Huang, H. Wechsler. Visual routines for eye location using learning and evolution. IEEETransactions on Evolutionary Computation[J],2000,4(1):73-82.
[204] E. K. Burke, M. Hyde, G. Kendall, G. Ochoa, E. Ozcan, R. Qu, A survey ofhyper-heuristics[R],2009.
[205] J. J. Liang, P. N. Suganthan, K. Deb. Novel composition test functions for numerical globaloptimization. IEEE Swarm Intelligence Symposium[C],2005:68-75.
[206]N. Hansen, A. Ostermeier. Completely derandomized self-adaptation in evolution strategies.Evolutionary Computation[J],2001,9(2):159-195.
[207] R. Mallipeddi, P. N. Suganthan. Ensemble of constraint handling techniques. IEEETransactions on Evolutionary Computation[J],2010,14(4):561-579.
[208] F. Peng, K. Tang, G. L. Chen, X. Yao. Population-based algorithm portfolios for numericaloptimization. IEEE Transactions on Evolutionary Computation[J],2010,14(5):782-800.
[209] P. K. Tripathi, S. Bandyopadhyay, S. K. Pal. Multi-objective particle swarm optimizationwith time variant inertia and acceleration coefficients. Information Sciences[J],2007,177(22):5033-5049.
[210] S. Das, P. N. Suganthan. Differential evolution: a survey of the state-of-the-art. IEEETransactions on Evolutionary Computation[J],2011,15(1):27-54.
[211] E. Mininno, F. Neri, F. Cupertino, D. Naso. Compact differential evolution. IEEETransactions on Evolutionary Computation[J],2011,15(1):32-54.
[212] B. Dorronsoro, P. Bouvry. Improving classical and decentralized differential evolution withnew mutation operator and population topologies. IEEE Transactions on EvolutionaryComputation[J],2011,15(1):67-98.
[213] M. G. Epitropakis, D. K. Tasoulis, N. G. Pavlidis, V. P. Plagianakos, M. N. Vrahatis.Enhancing differential evolution utilizing proximity-based mutation operators. IEEETransactions on Evolutionary Computation[J],2011,15(1):99-119.
[214] J. Lampinen, I. Zelinka. On stagnation of the differential evolution algorithm. The6thInternational Nendel Conference on Soft Computing[C],2000:76-83.
[215] R. G mperle, S. D. Müller, P. Koumoutsakos. A parameter study for differential evolution.Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation[J],2002,10(1):293-298.
[216] S. Das, A. Konar, U. K. Chakraborty. Two improved differential evolution schemes forfaster global search. GECCO2005: Genetic and Evolutionary Computation Conference[C],2005:991-998.
[217] J. M. Whitacre, T. Q. Pham, R. A. Sarker. Credit assignment in adaptive evolutionaryalgorithms. The8th Annual Genetic and Evolutionary Computation Conference[C],2006:1353-1360.
[218] D. Thierens. An adaptive pursuit strategy for allocating operator probabilities. The Geneticand Evolutionary Computation Conference[C],2005:1546.
[219] L. N. De Castro, J. Timmis. An artificial immune network for multimodal functionoptimization. IEEE World Congress on Computational Intelligence[C],2002:699-704.
[220] E. Clark, A. Hone, J. Timmis. A markov chain model of the b-cell algorithm. ArtificialImmune Systems[J],2005,318-330.
[221] M. G. Gong, L. C. Jiao, L. N. Zhang, H. F. Du. Immune secondary response and clonalselection inspired optimizers. Progress in Natural Science[J],2009,19(2):237-253.
[222] E. K. Burke, M. R. Hyde, G. Kendall, G. Ochoa. Exploring hyper-heuristic methodologieswith genetic programming. Computational Intelligence[J],2009,1(3):177-201.
[223] P. Ross, J. G. Marfn-Blazquez. Constructive hyper-heuristics in class timetabling. IEEECongress on Evolutionary Computation[C],2005:1493-1500.
[224] E. K. Burke, G. Kendall, E. Soubeiga. A tabu-search hyperheuristic for timetabling androstering. Journal of Heuristics[J],2003,9(6):451-470.
[225] E. K. Burker, M. Hyde, G. Kendall, G. Ochoa, E. zcan, J. R. Woodward. A classificationof hyper-heuristic approaches. Handbook of Metaheuristics[J],2010,146(25):449-468.
[226] V. Cutello, G. Narzisi, G. Nicosia, M. Pavone. An immunological algorithm for globalnumerical optimization. The7th International Conference on Evolution Artificielle[C],2006:284-295.
[227] B. Parley, D. Scott. The Lanczos algorithm with selective orthogonalization[M].1979:217-238.
[228]M. Crouzeix, B. Philippe. The davidson method. SIAM Journal on Scientific Computing[J],1994,229(2):453-460.
[229]李湘清,孙秀霞,夏岩. Multi Agent动态分组模型用于多无人机协同任务分配.系统仿真学报[J],2010,22(05):1266-1269.
[230]朱毅,张涛,程农,宋靖雁.多UAV协同任务规划研究.系统仿真学报[J],2008,20(S2):194-199.
[231]叶媛媛.多UCAV协同任务规划方法研究,[博士学位论文].国防科学技术大学.2005.
[232]龙涛,朱华勇,沈林成.多UCAV协同中基于协商的分布式任务分配研究.宇航学报[J],2006,27(03):457-462.
[233]廖沫,陈宗基,周锐.基于MAS的多UAV协同任务分配设计与仿真.系统仿真学报[J],2007,19(10):2313-2317.
[234]宋敏,魏瑞轩,冯志明.基于差分进化算法的异构多无人机任务分配.系统仿真学报[J],2010,22(07):1706-1710.
[235]国博,王社伟,陶军.基于改进粒子群算法的多无人机任务分配研究.计算机仿真[J],2009,26(07):62-64+153.
[236]叶媛媛,闵春平,沈林成,朱华勇.基于混合遗传算法的多UCAV协同任务分配方法.东南大学学报(自然科学版)[J],2005,35(S2):53-58.
[237]周锐,吴雯漫,罗广文.自主多无人机的分散化协同控制.航空学报[J],2008,29(S1):26-32.
[238]金久才,张杰,官晟,王岩峰.自推进粒子群的空间同步平行编队控制.控制理论与应用[J],2011,28(04):587-590.
[239]李雪松,李颖晖,张水清,李霞,徐浩军.基于视线的无人机鲁棒自适应编队控制设计.飞行力学[J],2011,29(06):31-35.
[240]朱战霞,郑莉莉.无人机编队飞行控制器设计.飞行力学[J],2007,25(04):22-24.
[241]樊琼剑,杨忠,方挺,沈春林.多无人机协同编队飞行控制的研究现状.航空学报[J],2009,30(04):683-691.
[242]秦硕,朱凡,刘永学,麻忠文.多UAV协同路径规划研究.系统仿真学报[J],2008,20(23):6356-6358+6364.
[243]包勇,符小卫,高晓光.势场理论的多无人机协同路径规划方法.火力与指挥控制[J],2012,37(03):10-12.
[244]丁琳,高晓光,王健,王娜.针对突发威胁的无人机多机协同路径规划的方法.火力与指挥控制[J],2005,30(07):7-10.
[245] A. S. Manne. A target-assignment problem. Operations Research[J],1958,6(1):346–351.
[246] J. C. Braford, Determination of optimal assignment of a weapon system to severaltargets[R],1961.
[247] S. Matlin. A review of the literature on the missile-allocation problem. OperationsResearch[J],1970,18(2):334-373.
[248] O. Kwon, K. Lee, D. Kang, S. Park. A branch-and-price algorithm for a targeting problem.Naval Research Logistics[J],2007,54(7):732-741.
[249] G. K. E. Optimal resource allocation using genetic algorithms. Naval Review[J],1993,42(21):174–175.
[250] A. M. Madni, M. Andrecut. Efficient heuristic approaches to the weapon-target assignmentproblem. Journal of Aerospace Computing Information and Communication[J],2009,6(6):405-414.
[251] Y. X. Wang, L. J. Qian, Z. Guo, L. F. Ma. Weapon target assignment problem satisfyingexpected damage probabilities based on ant colony algorithm. Journal of SystemsEngineering and Electronics[J],2008,19(5):939-944.
[252] M. Z. Lee. Constrained weapon-target assignment: enhanced very large scale neighborhoodsearch algorithm. IEEE Transactions on Systems Man and Cybernetics Part A-Systems andHumans[J],2010,40(1):198-204.
[253] H. Cai, J. Liu, Y. Chen, H. Wang. Survey of the research on dynamic weapon-targetassignment problem. Journal of Systems Engineering and Electronics[J],2006,17(3):559-565.
[254] J. Chen, B. Xin, Z. H. Peng, L. H. Dou, J. Zhang. Evolutionary decision-makings for thedynamic weapon-target assignment problem. Science in China Series F-InformationSciences[J],2009,52(11):2006-2018.
[255] B. Xin, J. Chen, Z. H. Peng, L. H. Dou, J. Zhang. An efficient rule-based constructiveheuristic to solve dynamic weapon-target assignment problem. IEEE Transactions onSystems Man and Cybernetics Part A-Systems and Humans[J],2011,41(3):598-606.
[256]田菁,沈林成.多基地多无人机协同侦察问题研究.航空学报[J],2007,28(04):913-921.
[257]田菁.多无人机协同侦察任务规划问题建模与优化技术研究,[博士学位论文].国防科学技术大学.2007.
[258]王慧林,邱涤珊,黄小军,马满好,祝江汉.面向区域覆盖的电子侦察卫星规划方法研究.兵工学报[J],2011,32(11):1365-1372.
[259]彭辉.分布式多无人机协同区域搜索中的关键问题研究,[博士学位论文].国防科学技术大学.2009.
[260]李俨,董玉娜.基于SA-DPSO混合优化算法的协同空战火力分配.航空学报[J],2010,31(03):626-631.
[261]罗德林,段海滨,吴顺详,李茂青.基于启发式蚁群算法的协同多目标攻击空战决策研究.航空学报[J],2006,27(06):1166-1170.
[262]彭星光,高晓光,魏小丰.基于贝叶斯优化算法的UCAV编队对联合目标的协同攻击研究.系统仿真学报[J],2008,20(10):2693-2696.
[263]高晓光,王云辉,李波.基于综合效能的火力/电子战协同攻击决策研究.系统工程与电子技术[J],2007,29(12):2081-2084.
[264]蓝伟华,赵春玲.空对空多机协同攻击多个目标的战术决策研究.火力与指挥控制[J],1999,24(01):31-34.
[265]蓝伟华,赵春玲.空对空多机协同攻击多个目标战术决策研究.电光与控制[J],1999,01(01):1-5.
[266]盛蔚,李建勋,佟明安.微分对策论在多机协同攻击中的应用研究.系统工程与电子技术[J],1998,01(03):8-12.
[267]王磊.协同攻击任务规划认知演化计算研究,[博士学位论文].国防科学技术大学.2010.
[268]王磊,黄寒砚,吴红,杨峰,王维平.巡航导弹协同攻击体系目标仿真优化.系统仿真学报[J],2011,23(09):1866-1871.
[269]黄郡,单洪,满毅.基于区域覆盖的协同干扰任务分配模型及算法.兵工学报[J],2011,32(06):725-732.
[270] T. L. Saaty, L. G. Vargas. The seven pillars of the analytic hierarchy process. Models,Methods, Concepts&Applications of the Analytic Hierarchy Process[J],2001,23(08):27-46.
[271] Q. K. Pan, L. Wang, L. Gao, W. D. Li. An effective hybrid discrete differential evolutionalgorithm for the flow shop scheduling with intermediate buffers. Information Sciences[J],2011,181(3):668-685.
[272] L. Wang, Q. K. Pan, P. N. Suganthan, W. H. Wang, Y. M. Wang. A novel hybrid discretedifferential evolution algorithm for blocking flow shop scheduling problems. Computers&Operations Research[J],2010,37(3):509-520.
[273] Q. K. Pan, M. F. Tasgetiren, Y. C. Liang. A discrete differential evolution algorithm for thepermutation flowshop scheduling problem. Computers&Industrial Engineering[J],2008,55(4):795-816.
[274] D. B. Fogel. An introduction to simulated evolutionary optimization. IEEE Transactions onEvolutionary Computation[J],1994,5(1):3-14.
[275] T. L. Saaty. How to make a decision: the analytic hierarchy process. European Journal ofOperational Research[J],1990,48(1):9-26.
[276] T. L. Saaty. A scaling method for priorities in hierarchical structures. Journal ofMathematical Psychology[J],1977,15(3):234-281.
[277]林闯,胡杰,孔祥震.用户体验质量(QoE)的模型与评价方法综述.计算机学报[J],2012,35(01):1-15.
[278]刘川意,汪东升.基于HPCC和层次分析法的高性能计算系统评价模型.软件学报[J],2007,18(04):1039-1046.
[279] L. Mikhailov, P. Tsvetinov. Evaluation of services using a fuzzy analytic hierarchy process.Applied Soft Computing[J],2004,5(1):23-33.
[280]王文彬,孙其博,杨放春. MANET下环境感知的服务可用性量化评估模型.计算机研究与发展[J],2012,49(03):558-564.
[281]熊润群,罗军舟,宋爱波,金嘉晖.云计算环境下QoS偏好感知的副本选择策略.通信学报[J],2011,32(07):93-102.
[282] M. F. Tasgetiren, Q. K. Pan, Y. C. Liang, P. N. Suganthan. A discrete differential evolutionalgorithm for the total earliness and tardiness penalties with a common due date on asingle-machine. IEEE Symposium on Computational Intelligence in Scheduling[C],2007:271-278.
[283] Q. K. Pan, M. F. Tasgetiren, Y. C. Liang. Minimizing total earliness and tardiness penaltieswith a common due date on a single-machine using a discrete particle swarm optimizationalgorithm. Ant Colony Optimization and Swarm Intelligence[J],2006,4150(24):460-467.
[284] Y. Xue, Y. Zhuang, T. Q. Ni, S. R. Ni. One improved genetic algorithm applied in theproblem of dynamic jamming resource scheduling with multi-objective and multi-constraint.IEEE Fifth international conference on Bio-inspored Computing:Theories andApplications[C],2010:708-712.
[285] X. F. Zhai, Y. Zhuang. IIGA based algorithm for cooperative jamming resource allocation.Asia Pacific Conference on Postgraduate Research[C],2009:368-371.
[286] X. J. Bi, J. Xiao. Classification-based self-adaptive differential evolution with fast andreliable convergence performance. Soft Computing[J],2011,15(8):1581-1599.
[287] J. Brest, B. Boskovic, V. Zumer. An improved self-adaptive differential evolution algorithmin single objective constrained real-parameter optimization. IEEE Congress on EvolutionaryComputation[C],2010:1-8.
[288] J. Brest, M. S. Maucec. Self-adaptive differential evolution algorithm using population sizereduction and three strategies. Soft Computing[J],2011,15(11):2157-2174.
[289] F. Peng, K. Tang, G. L. Chen, X. Yao. Multi-start JADE with knowledge transfer fornumerical optimization.2009IEEE Congress on Evolutionary Computation[C],2009:1889-1895.
[290] Y. Wang, J. Z. Zhou, C. Zhou, Y. Q. Wang, H. Qin, Y. L. Lu. An improved self-adaptivePSO technique for short-term hydrothermal scheduling. Expert Systems with Applications[J],2012,39(3):2288-2295.
[291] S. Z. Zhao, P. N. Suganthan, S. Das. Self-adaptive differential evolution with modifiedmulti-trajectory search for CEC'2010large scale optimization. Swarm, Evolutionary, andMemetic Computing[C],2010:1-10.
[292] S. Z. Zhao, P. N. Suganthan, S. Das. Self-adaptive differential evolution withmulti-trajectory search for large-scale optimization. Soft Computing[J],2011,15(11):2175-2185.
[293] D. H. Wolpert, W. G. Macready. No free lunch theorems for optimization. IEEETransactions on Evolutionary Computation[J],1997,1(1):67-82.
[294] M. F. Tasgetiren, Q. K. Pan, P. N. Suganthan, Y. C. Liang. A discrete differential evolutionalgorithm for the no-wait flowshop scheduling problem with total flowtime criterion. IEEESymposium on Computational Intelligence in Scheduling[C],2007:251-258.
[295] L. Davis. Applying adaptive algorithms to epistatic domains. International Joint Conferenceon Artificial Intelligence[C],1985:161-163.
[296] G. D. E., L. R. Alleles, loci and the traveling salesman problem. International Conferenceon Genetic Algorithms and Their Applications[C],1985:151-159.
[297] Q. K. Pan, M. F. Tasgetiren, P. N. Suganthan, T. J. Chua. A discrete artificial bee colonyalgorithm for the lot-streaming flow shop scheduling problem. Information Sciences[J],2011,181(12):2455-2468.
[2] S. P. Lloyd, H. S. Witsenhausen. Weapons allocation is NP-complete. The1986SummerConference on Simulation[C],1986:1054-1058.
[3]范铁虎,秦贵和,赵启.用于求解连续优化问题的均匀设计和改造BLX-α的分散搜索算法.计算机研究与发展[J],2011,48(06):1049-1058.
[4]钟伟才,刘静,刘芳,焦李成.组合优化多智能体进化算法.计算机学报[J],2004,27(10):1341-1354.
[5] W. D. Song, C. W. Zhao, J. X. Huo. Improved differential evolution algorithm for solvingWTA problem. International Conference on Energy and Environmental Science[C],2011:1348-1353.
[6] L. Wang, G. Zhou, Y. Xu, M. Liu. An enhanced Pareto-based artificial bee colony algorithmfor the multi-objective flexible job-shop scheduling. International Journal of AdvancedManufacturing Technology[J],2012,60(09):1111-1123.
[7] D. Martens, B. Baesens, T. Fawcett. Editorial survey: swarm intelligence for data mining.Machine Learning[J],2011,82(1):1-42.
[8] B. Alatas, E. Akin, A. Karci. Modenar: Multi-objective differential evolution algorithm formining numeric association rules. Applied Soft Computing[J],2008,8(1):646-656.
[9] T. Y. Wu, G. H. Liaw, S. W. Huang, W. T. Lee, C. C. Wu. A GA-based mobile RFIDlocalization scheme for internet of things. Personal and Ubiquitous Computing[J],2012,16(3):245-258.
[10] S. Pandey, L. L. Wu, S. M. Guru, R. Buyya. A particle swarm optimization-based heuristicfor scheduling workflow applications in cloud computing environments. The24th IEEEInternational Conference on Advanced Information Networking and Applications[C],2011:400-407.
[11] S. B. Zhong, Z. S. He. The scheduling algorithm of grid task based on PSO and cloud model.Advanced Measurement and Test, Parts1and2[C],2010:1487-1492.
[12] P. Y. Yin. A discrete particle swarm algorithm for optimal polygonal approximation of digitalcurves. Journal of Visual Communication and Image Representation[J],2004,15(2):241-260.
[13] J. F. Connolly, E. Granger, R. Sabourin. Evolution of heterogeneous ensembles throughdynamic particle swarm optimization for video-based face recognition. PatternRecognition[J],2012,45(7):2460-2477.
[14] O. Gusikhin, D. Filev, N. Rychtyckyj. Intelligent vehicle systems-applications and newtrends. Proceedings of the Third International Conference on Informatics in Control,Automation and Robotics: Robotics and Automation[C],2006:IS11-IS18.
[15] Q. L. Wei, H. G. Zhang, J. Dai. Model-free multiobjective approximate dynamicprogramming for discrete-time nonlinear systems with general performance index functions.Neurocomputing[J],2009,72(07):1839-1848.
[16] H. B. He, A. Kusiak. Special issue on computational intelligence in smart grid. IEEEComputational Intelligence Magazine[J],2011,6(3):12-64.
[17] C. Lv, M. D. Ma, H. Li, J. F. Ma, B. Niu. Security enhancement of thecommunication-efficient AUTHMAC_DH protocols. Security and CommunicationNetworks[J],2012,5(6):674-680.
[18] D. Dasgupta. Special issue on computational intelligence in cyber security-Preface. IEEEComputational Intelligence Magazine[J],2008,3(2):21-21.
[19]刘晓平,何涛,黄永红,唐卫清,刘慎权.基于优先表的工程约束求解算法研究.软件学报[J],1999,10(11):1127-1131.
[20]傅英定,成孝予,唐应辉.最优化理论与方法[M].2007:120-247.
[21]王玲,薄列峰,刘芳,焦李成.最小二乘隐空间支持向量机.计算机学报[J],2005,28(08):1302-1307.
[22] L. M. Rios, N. V. Sahinidis. Derivative-free optimization: A review of algorithms andcomparison of software implementations. Journal of Global Optimization[J],2011,12(12):1-47.
[23] D. E. Goldberg. Genetic algorithms in search, optimization, and machine learning[M].1989:45-105.
[24] S. Kirkpatrick, C. D. Gelatt Jr, M. P. Vecchi. Optimization by simulated annealing.Science[J],1983,220(4598):671-680.
[25] F. Glover. Tabu search: a tutorial. Interfaces[J],1990,20(4):74-94.
[26] X. Yao, Y. Liu, G. M. Lin. Evolutionary programming made faster. IEEE Transactions onEvolutionary Computation[J],1999,3(2):82-102.
[27] K. F. Liem. Evolutionary strategies and morphological innovations: cichlid pharyngeal jaws.Systematic Biology[J],1973,22(4):425-441.
[28] R. R. Barton, J. S. Ivey Jr. Nelder-mead simplex modifications for simulation optimization.Management Science[J],1996,34(65):954-973.
[29] M. Dorigo, L. M. Gambardella. Ant colony system: a cooperative learning approach to thetraveling salesman problem. IEEE Transactions on Evolutionary Computation[J],1997,31(11):53-66.
[30] J. Kennedy, R. Eberhart. Particle swarm optimization. IEEE International Conference onNeural Networks[C],1995:1942-1948.
[31] U. Aickelin, J. Greensmith, J. Twycross. Immune system approaches to intrusion detection–areview. Artificial Immune Systems[J],2004,316-329.
[32] R. Storn, K. Price. Differential evolution–a simple and efficient heuristic for globaloptimization over continuous spaces. Journal of Global Optimization[J],1997,11(4):341-359.
[33] D. Karaboga, An idea based on honey bee swarm for numerical optimization[R],2005.
[34] J. D. Knowles, D. W. Corne. M-PAES: a memetic algorithm for multiobjective optimization.IEEE Congress on Evolutionary Computation[C],2000:325-332.
[35]董文永,张文生,于瑞国.求解组合优化问题伊藤算法的收敛性和期望收敛速度分析.计算机学报[J],2011,34(4):636-646.
[36] H. Kubiny. Variable selection in QSAR studies. I. An evolutionary algorithm. QuantitativeStructure‐Activity Relationships[J],1994,13(3):285-294.
[37]王瑜.基于多方法融合的进化算法研究,[博士学位论文].中国科学技术大学.2011.
[38] C. A. Mendez, J. Cerda, I. E. Grossmann, I. Harjunkoski, M. Fahl. State-of-the-art review ofoptimization methods for short-term scheduling of batch processes. Computers&ChemicalEngineering[J],2006,30(06):913-946.
[39] N. V. Sahinidis. Optimization under uncertainty: state-of-the-art and opportunities.Computers&Chemical Engineering[J],2004,28(06):971-983.
[40] K. Deb, A. Pratap, S. Agarwal, T. Meyarivan. A fast and elitist multiobjective geneticalgorithm: NSGA-II. IEEE Transactions on Evolutionary Computation[J],2002,6(2):182-197.
[41] C. M. Fonseca, P. J. Fleming. Multiobjective optimization and multiple constraint handlingwith evolutionary algorithms-Part I: A unified formulation. IEEE Transactions on SystemsMan and Cybernetics Part a-Systems and Humans[J],1998,28(1):26-37.
[42] E. Zitzler, K. Deb, L. Thiele. Comparison of multiobjective evolutionary algorithms:empirical results. Evolutionary Computation[J],2000,8(2):173-195.
[43] E. Zitzler, L. Thiele. Multiobjective evolutionary algorithms: A comparative case study andthe strength pareto approach. IEEE Transactions on Evolutionary Computation[J],1999,3(4):257-271.
[44] E. Zitzler, L. Thiele, M. Laumanns, C. M. Fonseca, V. G. da Fonseca. Performanceassessment of multiobjective optimizers: An analysis and review. IEEE Transactions onEvolutionary Computation[J],2003,7(2):117-132.
[45] J. H. Holland. Adaptation in natural and artificial systems[M].1975:37-69.
[46] F. Glover. Tabu search-part I. ORSA Journal on computing[J],1989,1(3):190-206.
[47] R. Eberhart, J. Kennedy. A new optimizer using particle swarm theory. Proceeding of theSixth International Symposium on Micro Machine and Human Science[C],1995:39-43.
[48] M. Dorigo, M. Birattari, T. Stutzle. Ant colony optimization. IEEE ComputationalIntelligence Magazine[J],2006,1(4):28-39.
[49] L. N. De Castro, F. J. Von Zuben. Learning and optimization using the clonal selectionprinciple. IEEE Transactions on Evolutionary Computation[J],2002,6(3):239-251.
[50] R. Storn, K. Price. Differential evolution-a simple and efficient adaptive scheme for globaloptimization over continuous spaces. International computer scienceinstitute-publications-TR[C],1995:1-12.
[51] A. K. Qin, V. L. Huang, P. N. Suganthan. Differential Evolution Algorithm With StrategyAdaptation for Global Numerical Optimization. Ieee Transactions on EvolutionaryComputation[J],2009,13(2):398-417.
[52]郭鹏.差分进化算法改进研究,[博士学位论文].天津大学.2012.
[53] J. Chen, B. Xin, Z. Peng, F. Pan. Statistical learning makes the hybridization of particleswarm and differential evolution more efficient—a novel hybrid optimizer. Science in ChinaSeries F: Information Sciences[J],2009,52(7):1278-1282.
[54] S. Das, S. Dasgupta, A. Biswas, A. Abraham. Automatic circle detection on images withannealed differential evolution. Eighth International Conference on Hybrid IntelligentSystems[C],2008:684-689.
[55] W. Gong, Z. Cai, C. X. Ling. DE/BBO: a hybrid differential evolution withbiogeography-based optimization for global numerical optimization. Soft Computing[J],2010,15(4):645-665.
[56] S. Rahnamayan, H. R. Tizhoosh, M. M. Salama. Opposition-based differential evolution.IEEE Transactions on Evolutionary Computation[J],2008,12(1):64-79.
[57] J. Teo. Exploring dynamic self-adaptive populations in differential evolution. SoftComputing[J],2006,10(8):673-686.
[58] M. Ali, M. Pant, V. Singh. Two modified differential evolution algorithms and theirapplications to engineering design problems. World Journal of Modelling and Simulation[J],2010,6(1):72-80.
[59] S. Das, A. Abraham, U. K. Chakraborty, A. Konar. Differential Evolution Using aNeighborhood-Based Mutation Operator. IEEE Transactions on EvolutionaryComputation[J],2009,13(3):526-553.
[60] J. Brest, S. Greiner, B. Boskovic, M. Mernik, V. Zumer. Self-adapting control parameters indifferential evolution: a comparative study on numerical benchmark problems. IEEETransactions on Evolutionary Computation[J],2006,10(6):646-657.
[61]常彦鑫,高正红.自适应差分进化算法在气动优化设计中的应用.航空学报[J],2009,30(09):1590-1596.
[62]陈秋霞,俞立,黄骅.基于RVM的非线性预测控制及在聚丙烯牌号切换中的应用.控制与决策[J],2010,25(08):1241-1245.
[63]吴斌,蔡红,樊树海,蒋南云.双倍体差分进化粒子群算法在VRPSDP中的应用研究.系统工程理论与实践[J],2010,30(03):520-526.
[64]徐新黎,施莉娜,范丽霞,王万良.基于广义粗糙集的流程工业车间调度研究.控制与决策[J],2012,27(01):8-14.
[65] R. Zhang, S. J. Song, C. Wu. A hybrid differential evolution algorithm for job shopscheduling problems with expected total tardiness criterion. Applied Soft Computing[J],2013,13(3):1448-1458.
[66] G. X. Zhang, J. X. Cheng, M. Gheorghe, Q. Meng. A hybrid approach based on differentialevolution and tissue membrane systems for solving constrained manufacturing parameteroptimization problems. Applied Soft Computing[J],2013,13(3):1528-1542.
[67] A. Ponsich, C. A. C. Coello. A hybrid differential evolution-tabu search algorithm for thesolution of job-shop scheduling problems. Applied Soft Computing[J],2013,13(1):462-474.
[68] X. T. Li, M. H. Yin. An opposition-based differential evolution algorithm for permutationflow shop scheduling based on diversity measure. Advances in Engineering Software[J],2013,55(01):10-31.
[69] G. L. Deng, X. S. Gu. A hybrid discrete differential evolution algorithm for the no-idlepermutation flow shop scheduling problem with makespan criterion. Computers&Operations Research[J],2012,39(9):2152-2160.
[70] D. Davendra, I. Zelinka, M. Bialic-Davendra, R. Senkerik, R. Jasek. Clustered enhanceddifferential evolution for the blocking flow shop scheduling problem. Central EuropeanJournal of Operations Research[J],2012,20(4):679-717.
[71] A. R. Yildiz. A new hybrid differential evolution algorithm for the selection of optimalmachining parameters in milling operations. Applied Soft Computing[J],2013,13(3):1561-1566.
[72] P. Mesejo, R. Ugolotti, F. Di Cunto, M. Giacobini, S. Cagnoni. Automatic hippocampuslocalization in histological images using Differential Evolution-based deformable models.Pattern Recognition Letters[J],2013,34(3):299-307.
[73] H. Kazemipoor, R. Tavakkoli-Moghaddam, P. Shahnazari-Shahrezaei, A. Azaron. Adifferential evolution algorithm to solve multi-skilled project portfolio scheduling problems.International Journal of Advanced Manufacturing Technology[J],2013,64(5-8):1099-1111.
[74]陈霄. DNA遗传算法及应用研究,[博士学位论文].浙江大学.2010.
[75] C. Garcia-Martinez, M. Lozano, F. Herrera, D. Molina, A. Sánchez. Global and localreal-coded genetic algorithms based on parent-centric crossover operators. European Journalof Operational Research[J],2008,185(3):1088-1113.
[76] K. Deep, M. Thakur. A new crossover operator for real coded genetic algorithms. AppliedMathematics and Computation[J],2007,188(1):895-911.
[77] K. Deep, M. Thakur. A new mutation operator for real coded genetic algorithms. AppliedMathematics and Computation[J],2007,193(1):211-230.
[78] Q. Zhang, J. Sun, E. Tsang. An evolutionary algorithm with guided mutation for themaximum clique problem. IEEE Transactions on Evolutionary Computation,[J],2005,9(2):192-200.
[79] P. Chootinan, A. Chen. Constraint handling in genetic algorithms using a gradient-basedrepair method. Computers&Operations Research[J],2006,33(8):2263-2281.
[80] S. Venkatraman, G. G. Yen. A generic framework for constrained optimization using geneticalgorithms. IEEE Transactions on Evolutionary Computation[J],2005,9(4):424-435.
[81] Y. Wang, Z. Cai, G. Guo, Y. Zhou. Multiobjective optimization and hybrid evolutionaryalgorithm to solve constrained optimization problems. IEEE Transactions on Systems, Man,and Cybernetics, Part B: Cybernetics[J],2007,37(3):560-575.
[82] C. M. Fonseca, P. J. Fleming. Genetic algorithms for multiobjective optimization:Formulation, discussion and generalization. Proceedings of the fifth international conferenceon genetic algorithms[C],1993:416-423.
[83] N. Srinivas, K. Deb. Muiltiobjective optimization using nondominated sorting in geneticalgorithms. Evolutionary Computation[J],1994,2(3):221-248.
[84] J. Horn, N. Nafpliotis, D. E. Goldberg. A niched Pareto genetic algorithm for multiobjectiveoptimization. Proceedings of the First IEEE Conference on Evolutionary Computation[C],1994:82-87.
[85] E. Zitzler, M. Laumanns, L. Thiele, E. Zitzler, E. Zitzler, L. Thiele, and L. Thiele,"SPEA2:Improving the strength Pareto evolutionary algorithm," ed: Eidgen ssische TechnischeHochschule Zürich (ETH), Institut für Technische Informatik und Kommunikationsnetze(TIK),2001.
[86] J. D. Knowles, D. W. Corne. Approximating the nondominated front using the Paretoarchived evolution strategy. Evolutionary Computation[J],2000,8(2):149-172.
[87] D. W. Corne, J. D. Knowles, M. J. Oates. The Pareto envelope-based selection algorithm formultiobjective optimization. Parallel Problem Solving from Nature PPSN VI[C],2000:839-848.
[88] D. W. Corne, N. R. Jerram, J. D. Knowles, M. J. Oates. PESA-II: Region-based selection inevolutionary multiobjective optimization. Proceedings of the Genetic and EvolutionaryComputation Conference[C],2001:1-8.
[89] M. Erickson, A. Mayer, J. Horn. The niched pareto genetic algorithm applied to the designof groundwater remediation systems. Evolutionary Multi-Criterion Optimization[C],2001:681-695.
[90] K. Deb, S. Agrawal, A. Pratap, T. Meyarivan. A fast elitist non-dominated sorting geneticalgorithm for multi-objective optimization: NSGA-II. Lecture Notes in Computer Science[J],2000,1917(21):849-858.
[91] B. Mansoornejad, N. Mostoufi, F. Jalali-Farahani. A hybrid GA–SQP optimization techniquefor determination of kinetic parameters of hydrogenation reactions. Computers&ChemicalEngineering[J],2008,32(7):1447-1455.
[92] X. Luo, Q.-Y. Wen, G. Fieg. A hybrid genetic algorithm for synthesis of heat exchangernetworks. Computers&Chemical Engineering[J],2009,33(6):1169-1181.
[93] H. Chen, N. S. Flann, D. W. Watson. Parallel genetic simulated annealing: a massivelyparallel SIMD algorithm. IEEE Transactions on Parallel and Distributed Systems[J],1998,9(2):126-136.
[94] Y.-T. Kao, E. Zahara. A hybrid genetic algorithm and particle swarm optimization formultimodal functions. Applied Soft Computing[J],2008,8(2):849-857.
[95] B.-B. Li, L. Wang. A hybrid quantum-inspired genetic algorithm for multiobjective flowshop scheduling. IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics[J],2007,37(3):576-591.
[96] X. H. Shi, Y. C. Liang, H. P. Lee, C. Lu, L. M. Wang. An improved GA and a novelPSO-GA-based hybrid algorithm. Information Processing Letters[J],2005,93(5):255-261.
[97]冯晓强,宋笔锋,李占科,桑建华.超声速飞机低声爆布局混合优化方法研究.航空学报[J],2012,33(01):1+10-11.
[98]王伟,赵俊杰,王辉.基于混合算法的MIMO雷达正交多相码设计.系统工程与电子技术[J],2013,35(02):294-298.
[99]司维超,韩维,史玮韦.基于HPSO算法和GA的舰载机甲板布放方法比较.系统工程与电子技术[J],2013,35(02):338-344.
[100] J. Torres, J. L. Guardado, F. Rivas-Davalos, S. Maximov, E. Melgoza. A genetic algorithmbased on the edge window decoder technique to optimize power distribution systemsreconfiguration. International Journal of Electrical Power&Energy Systems[J],2013,45(1):28-34.
[101] M. Mehrjoo, N. Khaji, M. Ghafory-Ashtiany. Application of genetic algorithm in crackdetection of beam-like structures using a new cracked Euler-Bernoulli beam element.Applied Soft Computing[J],2013,13(2):867-880.
[102] R. Koker. A genetic algorithm approach to a neural-network-based inverse kinematicssolution of robotic manipulators based on error minimization. Information Sciences[J],2013,222(01):528-543.
[103] D. S. Jung, C. Y. Kim. Finite element model updating on small-scale bridge model usingthe hybrid genetic algorithm. Structure and Infrastructure Engineering[J],2013,9(5):481-495.
[104] L. De Giovanni, G. Massi, F. Pezzella. An adaptive genetic algorithm for large-size openstack problems. International Journal of Production Research[J],2013,51(3):682-697.
[105] A. Brunetti. A fast fine-grained genetic algorithm for spectrum fitting: An application toX-ray spectra. Computer Physics Communications[J],2013,184(3):573-578.
[106] M. Dorigo, V. Maniezzo, A. Colorni, The ant system: an autocatalytic optimizingprocess[R],1991.
[107]葛连升.基于蚁群优化的组播路由算法研究,[博士学位论文].山东大学.2010.
[108] M. Dorigo, L. M. Gambardella. Ant colony system: A cooperative learning approach to thetraveling salesman problem. IEEE Transactions on Evolutionary Computation,[J],1997,31(11):53-66.
[109]陈崚,沈洁,秦玲,陈宏建.基于分布均匀度的自适应蚁群算法.软件学报[J],2003,14(08):1379-1387.
[110]黄国锐,曹先彬,王煦法.基于信息素扩散的蚁群算法.电子学报[J],2004,35(05):865-868.
[111]柯良军,冯祖仁,冯远静.有限级信息素蚁群算法.自动化学报[J],2006,32(02):296-303.
[112] F. Abbattista, N. Abbattista, L. Caponetti. An evolutionary and cooperative agents modelfor optimization. IEEE International Conference on Evolutionary Computation[C],1995:668-671.
[113]丁建立,陈增强,袁著祉.遗传算法与蚂蚁算法的融合.计算机研究与发展[J],2003,40(09):1351-1356.
[114] Z. J. Lee, C. Y. Lee. An immunity-based ant colony optimization algorithm for solvingweapon–target assignment problem. Applied Soft Computing[J],2002,02(02):39-47.
[115]姚锋,邢立宁.求解卫星地面站调度问题的演化学习型蚁群算法.系统工程与电子技术[J],2012,34(11):2270-2274.
[116]宋超,刘明,龚海刚,陈贵海,王晓敏.基于蚁群优化解决传感器网络中的能量洞问题.软件学报[J],2009,20(10):2729-2743.
[117]段海滨,丁全心,常俊杰,刘森琪.基于并行蚁群优化的多UCAV任务分配仿真平台.航空学报[J],2008,29(S1):192-197.
[118]马冠军,段海滨,刘森琪,于亚翔.基于MAX-MIN自适应蚁群优化的无人作战飞机航路规划.航空学报[J],2008,29(S1):243-248.
[119] C. J. Ting, C. H. Chen. A multiple ant colony optimization algorithm for the capacitatedlocation routing problem. International Journal of Production Economics[J],2013,141(1):34-44.
[120] E. Resendiz, L. A. Moncayo-Martinez, G. Solis. Binary ant colony optimization applied tovariable screening in the Mahalanobis-Taguchi System. Expert Systems with Applications[J],2013,40(2):634-637.
[121] X. J. Liu, H. Yi, Z. H. Ni. Application of ant colony optimization algorithm in processplanning optimization. Journal of Intelligent Manufacturing[J],2013,24(1):1-13.
[122] B. Y. Cheng, Q. Wang, S. L. Yang, X. X. Hu. An improved ant colony optimization forscheduling identical parallel batching machines with arbitrary job sizes. Applied SoftComputing[J],2013,13(2):765-772.
[123] J. Bai, G. K. Yang, Y. W. Chen, L. S. Hu, C. C. Pan. A model induced max-min ant colonyoptimization for asymmetric traveling salesman problem. Applied Soft Computing[J],2013,13(3):1365-1375.
[124]王宇嘉.多目标粒子群优化算法的全局搜索策略研究,[博士学位论文].上海交通大学.2008.
[125] J. Kennedy, R. Mendes. Population structure and particle swarm performance. Proceedingof the2002congress on evolutionary computation[C],2002:1671-1676.
[126] P. W. Moore, G. K. Venayagamoorthy. Evolving digital circuits using hybrid particle swarmoptimization and differential evolution. International Journal of Neural Systems[J],2006,16(3):163-178.
[127] R. Xu, G. K. Venayagamoorthy, D. C. Wunsch II. Modeling of gene regulatory networkswith hybrid differential evolution and particle swarm optimization. Neural Networks[J],2007,20(8):917-927.
[128] A. Chatterjee, P. Siarry. Nonlinear inertia weight variation for dynamic adaptation inparticle swarm optimization. Computers&Operations Research[J],2006,33(3):859-871.
[129] M. S. Arumugam, M. Rao. On the improved performances of the particle swarmoptimization algorithms with adaptive parameters, cross-over operators and root mean square(RMS) variants for computing optimal control of a class of hybrid systems. Applied SoftComputing[J],2008,8(1):324-336.
[130] X. Yang, J. Yuan, J. Yuan, H. Mao. A modified particle swarm optimizer with dynamicadaptation. Applied Mathematics and Computation[J],2007,189(2):1205-1213.
[131] P. Bajpai, S. Singh. Fuzzy adaptive particle swarm optimization for bidding strategy inuniform price spot market. IEEE Transactions on Power Systems[J],2007,22(4):2152-2160.
[132] A. Ratnaweera, S. K. Halgamuge, H. C. Watson. Self-organizing hierarchical particleswarm optimizer with time-varying acceleration coefficients. IEEE Transactions onEvolutionary Computation[J],2004,8(3):240-255.
[133] I. C. Trelea. The particle swarm optimization algorithm: convergence analysis andparameter selection. Information Processing Letters[J],2003,85(6):317-325.
[134]F. van den Bergh, A. P. Engelbrecht. A cooperative approach to particle swarm optimization.IEEE Transactions on Evolutionary Computation[J],2004,8(3):225-239.
[135] C. F. Juang. A hybrid of genetic algorithm and particle swarm optimization for recurrentnetwork design. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics[J],2004,34(2):997-1006.
[136]黄江涛,高正红,白俊强,赵轲,李静,许放.基于任意空间属性FFD技术的融合式翼稍小翼稳健型气动优化设计.航空学报[J],2013,34(01):37-45.
[137]王连锋,宋建社,王正元,曹继平.带硬时间窗的战场物资配送车辆路径优化.系统工程与电子技术[J],2013,35(01):1-10.
[138]刘晓,刘忠,侯文姝,许江湖.火力分配多目标规划模型的改进MOPSO算法.系统工程与电子技术[J],2013,35(02):326-330.
[139]刘钦,刘峥,刘韵佛,谢荣.多传感器优化部署下的机动目标协同跟踪算法.系统工程与电子技术[J],2013,35(02):304-309.
[140]郭霖瀚,卞洁辉,王乃超,康锐.基于PSO的方案阶段修理级别优化方法.系统工程与电子技术[J],2013,35(01):97-101.
[141] A. A. Altun, M. A. Sahman. Cost optimization of mixed feeds with the particle swarmoptimization method. Neural Computing&Applications[J],2013,22(2):383-390.
[142] Q. Feng, J. Zhang, X. Zhang, A. Hu. Optimizing well placement in a coalbed methanereservoir using the particle swarm optimization algorithm. International Journal of CoalGeology[J],2012,104(07):34-45.
[143] B. T. Jiang, F. Y. Zhao. Particle swarm optimization-based least squares support vectorregression for critical heat flux prediction. Annals of Nuclear Energy[J],2013,53(03):69-81.
[144] M. A. Mohandes. Modeling global solar radiation using Particle Swarm Optimization(PSO). Solar Energy[J],2012,86(11):3137-3145.
[145] N. Metropolis, A. W. Rosenbluth, M. N. Rosenbluth, A. H. Teller, E. Teller. Equation ofstate calculations by fast computing machines. The Journal of Chemical Physics[J],1953,21(1):1087-1092.
[146]赵松原.模拟退火结合正交分解算法的气动外形最优化设计,[博士学位论文].南京航空航天大学.2006.
[147]顾元宪,项宝卫,赵国忠.一种改进的连续变量全局优化模拟退火算法.系统工程理论与实践[J],2005,04(04):103-109.
[148]徐雷.一种改进的模拟退火组合优化法.信息与控制[J],1990,01(03):1-7.
[149]巩敦卫,曾现峰,张勇.基于改进模拟退火算法的机器人全局路径规划.系统仿真学报[J],2013,25(03):480-483+488.
[150]李凯,左春荣.基于模拟退火的多处理机调度优化算法研究.系统工程与电子技术[J],2009,31(12):2963-2967.
[151]李文,陈英武,李菊芳,姚锋.基于快速模拟退火的遥感数据处理调度方法.系统工程与电子技术[J],2011,33(02):334-338.
[152]梁毓明,徐立鸿.基于改进模拟退火混合算法的移动机器人全局路径规划.控制与决策[J],2010,25(02):237-240+245.
[153]刘斌,姜囡,井元伟.基于模拟退火算法的电力系统输电阻塞成本控制.控制与决策[J],2010,25(08):1184-1188.
[154]刘以安,倪天权,张秀辉,李游.模拟退火算法在雷达干扰资源优化分配中的应用.系统工程与电子技术[J],2009,31(08):1914-1917.
[155] A. Ekbal, S. Saha. Combining feature selection and classifier ensemble using amultiobjective simulated annealing approach: application to named entity recognition. SoftComputing[J],2013,17(1):1-16.
[156] S. S. Mohammadi, A. Hezarkhani, A. E. Tercan. Optimally locating additional drill holes inthree dimensions using grade and simulated annealing. Journal of the Geological Society ofIndia[J],2012,80(5):700-706.
[157] A. Roshani, M. Salehi, A. Esfandyari. A simulated annealing algorithm for multi-mannedassembly line balancing problem. Journal of Manufacturing Systems[J],2013,32(1):238-247.
[158] D. Karaboga, B. Gorkemli, C. Ozturk, N. Karaboga. A comprehensive survey: artificial beecolony (ABC) algorithm and applications. Artificial Intelligence Review[J],2012,1-37.
[159] B. Akay, D. Karaboga. Parameter tuning for the artificial bee colony algorithm[M].2009:608-619.
[160] L. Bao, J.-c. Zeng. Comparison and analysis of the selection mechanism in the artificial beecolony algorithm. Ninth International Conference on Hybrid Intelligent Systems[C],2009:411-416.
[161] A. Aderhold, K. Diwold, A. Scheidler, M. Middendorf. Artificial bee colony optimization:A new selection scheme and its performance[M].2010:283-294.
[162] P. Guo, W. Cheng, J. Liang. Global artificial bee colony search algorithm for numericalfunction optimization. Seventh International Conference on Natural Computation[C],2011:1280-1283.
[163] A. Rajasekhar, A. Abraham, M. Pant. Levy mutated artificial bee colony algorithm forglobal optimization. IEEE International Conference on Systems, Man, and Cybernetics[C],2011:655-662.
[164] W. P. zou, Y. L. Zhu, H. N. Chen, T. Ku. Clustering approach based on von neumanntopology artificial bee colony algorithm. LNCS: Advances in Soft Computing: Foundationsof Fuzzy Logic and Soft Computing[J],2011,4529(02):789-798.
[165]雷秀娟,田建芳.蛋白质相互作用网络的蜂群信息流聚类模型与算法.计算机学报[J],2012,35(01):134-145.
[166]张银雪,田学民,邓晓刚.基于改进人工蜂群算法的盲源分离方法.电子学报[J],2012,40(10):2026-2030.
[167]李鑫滨,刘磊,马锴.基于离散人工蜂群算法的认知无线电频谱分配.系统工程与电子技术[J],2012,34(10):2136-2141.
[168] R. Zhang, S. J. Song, C. Wu. A hybrid artificial bee colony algorithm for the job shopscheduling problem. International Journal of Production Economics[J],2013,141(1):167-178.
[169] A. R. Yildiz. Optimization of cutting parameters in multi-pass turning using artificial beecolony-based approach. Information Sciences[J],2013,220(22):399-407.
[170] A. Rubio-Largo, M. A. Vega-Rodriguez, J. A. Gomez-Pulido, J. M. Sanchez-Perez. Amultiobjective approach based on artificial bee colony for the static routing and wavelengthassignment problem. Soft Computing[J],2013,17(2):199-211.
[171] A. K. Qin, P. N. Suganthan. Self-adaptive differential evolution algorithm for numericaloptimization. IEEE Congress on Evolutionary Computation[C],2005:1785-1791.
[172] R. Mallipeddi, P. N. Suganthan, Q. K. Pan, M. F. Tasgetiren. Differential evolutionalgorithm with ensemble of parameters and mutation strategies. Applied Soft Computing[J],2011,11(2):1679-1696.
[173] Q. K. Pan, P. N. Suganthan, L. Wang, L. Gao, R. Mallipeddi. A differential evolutionalgorithm with self-adapting strategy and control parameters. Computers&OperationsResearch[J],2011,38(1):394-408.
[174] W. Y. Gong, Z. H. Cai, C. X. Ling, H. Li. Enhanced differential evolution with adaptivestrategies for numerical optimization. IEEE Transactions on Systems Man and CyberneticsPart B-Cybernetics[J],2011,41(2):397-413.
[175] Y. Wang, Z. X. Cai, Q. F. Zhang. Differential evolution with composite trial vectorgeneration strategies and control parameters. IEEE Transactions on EvolutionaryComputation[J],2011,15(1):55-66.
[176] Z. J. Lee, S. F. Su, C. Y. Lee. Efficiently solving general weapon-target assignment problemby genetic algorithms with greedy eugenics. IEEE Transactions on Systems Man andCybernetics Part B-Cybernetics[J],2003,33(1):113-121.
[177] B. Xin, J. Chen, J. A. Zhang, L. H. Dou, Z. H. Peng. Efficient decision makings fordynamic weapon-target assignment by virtual permutation and tabu search heuristics. IEEETransactions on Systems Man and Cybernetics Part C-Applications and Reviews[J],2010,40(6):649-662.
[178] Z. J. Lee, W. L. Lee. A hybrid search algorithm of ant colony optimization and geneticalgorithm applied to Weapon-Target Assignment problems[M].2003:278-285.
[179] R. K. Ahuja, A. Kumar, K. C. Jha, J. B. Orlin. Exact and heuristic algorithms for theweapon-target assignment problem. Operations Research[J],2007,55(6):1136-1146.
[180] M. Clerc, J. Kennedy. The particle swarm-explosion, stability, and convergence in amultidimensional complex space. IEEE Transactions on Evolutionary Computation[J],2002,6(1):58-73.
[181]R. Mendes, J. Kennedy, J. Neves. The fully informed particle swarm: simpler, maybe better.IEEE Transactions on Evolutionary Computation[J],2004,8(3):204-210.
[182] J. J. Liang, A. K. Qin, P. N. Suganthan, S. Baskar. Comprehensive learning particle swarmoptimizer for global optimization of multimodal functions. IEEE Transactions onEvolutionary Computation[J],2006,10(3):281-295.
[183] T. Fukuda, K. Mori, M. Tsukiyama. Immune networks using genetic algorithm for adaptiveproduction scheduling.15th IFAC world congress[C],1993:57-60.
[184] V. Cutello, G. Narzisi, G. Nicosia, M. Pavone. Clonal selection algorithms: a comparativecase study using effective mutation potentials. Lecture Notes in Computer Science[J],2005,3627(1):13-28.
[185] R. Mallipeddi, P. Suganthan. Differential evolution algorithm with ensemble of populationsfor global numerical optimization. OPSEARCH[J],2009,46(2):184-213.
[186] F. Neri, V. Tirronen. Recent advances in differential evolution: a survey and experimentalanalysis. Artificial Intelligence Review[J],2010,33(1-2):61-106.
[187] N. Noman, H. Iba. Accelerating differential evolution using an adaptive local search. IEEETransactions on Evolutionary Computation[J],2008,12(1):107-125.
[188] V. Tirronen, F. Neri, T. Rossi. Enhancing differential evolution frameworks by scale factorlocal search-Part I. IEEE Congress on Evolutionary Computation[C],2009:94-101.
[189] S. Rahnamayan, H. R. Tizhoosh, M. M. A. Salama. Opposition-based differential evolution.IEEE Transactions on Evolutionary Computation[J],2008,12(1):64-79.
[190] R. Mallipeddi, S. Mallipeddi, P. Suganthan. Ensemble strategies with adaptive evolutionaryprogramming. Information Sciences[J],2010,180(9):1571-1581.
[191] J. A. Vrugt, B. A. Robinson, J. M. Hyman. Self-adaptive multimethod search for globaloptimization in real-parameter spaces. IEEE Transactions on Evolutionary Computation[J],2009,13(2):243-259.
[192] J. A. Vrugt, B. A. Robinson. Improved evolutionary optimization from genetically adaptivemultimethod search. National Academy of Sciences of the United States of America[C],2007:708-711.
[193] Y. Wang, B. Li, T. Weise, J. Y. Wang, B. Yuan, Q. J. Tian. Self-adaptive learning basedparticle swarm optimization. Information Sciences[J],2011,181(20):4515-4538.
[194] B. Xu, Y. Zhuang, Y. Xue, Z. Wang. Self-adaptive learning based immune algorithm.Journal of Central South University[J],2012,19(4):1021-1031.
[195] J. M. Baldwin. A new factor in evolution. The American Naturalist[J],1896,30(355):536-553.
[196] M. G. Gong, L. C. Jiao, L. N. Zhang. Baldwinian learning in clonal selection algorithm foroptimization. Information Sciences[J],2010,180(8):1218-1236.
[197] V. Cutello, G. Nicosia, M. Pavone. Exploring the capability of immune algorithms: acharacterization of hypermutation operators[M].2004:263-276.
[198] P. A. D. De Castro, F. J. Von Zuben. BAIS: A bayesian artificial immune system for theeffective handling of building blocks. Information Sciences[J],2009,179(10):1426-1440.
[199] M. Gong, L. Jiao, H. Du, L. Bo. Multiobjective immune algorithm with nondominatedneighbor-based selection. Evolutionary Computation[J],2008,16(2):225-255.
[200] M. G. Gong, L. C. Jiao, X. R. Zhang. A population-based artificial immune system fornumerical optimization. Neurocomputing[J],2008,72(1-3):149-161.
[201] L. C. Jiao, Y. Y. Li, M. G. Gong, X. R. Zhang. Quantum-inspired immune clonal algorithmfor global optimization. IEEE Transactions on Systems Man and Cybernetics PartB-Cybernetics[J],2008,38(5):1234-1253.
[202] G. C. Luh, C. H. Chueh. A multi-modal immune algorithm for the job-shop schedulingproblem. Information Sciences[J],2009,179(10):1516-1532.
[203] J. Huang, H. Wechsler. Visual routines for eye location using learning and evolution. IEEETransactions on Evolutionary Computation[J],2000,4(1):73-82.
[204] E. K. Burke, M. Hyde, G. Kendall, G. Ochoa, E. Ozcan, R. Qu, A survey ofhyper-heuristics[R],2009.
[205] J. J. Liang, P. N. Suganthan, K. Deb. Novel composition test functions for numerical globaloptimization. IEEE Swarm Intelligence Symposium[C],2005:68-75.
[206]N. Hansen, A. Ostermeier. Completely derandomized self-adaptation in evolution strategies.Evolutionary Computation[J],2001,9(2):159-195.
[207] R. Mallipeddi, P. N. Suganthan. Ensemble of constraint handling techniques. IEEETransactions on Evolutionary Computation[J],2010,14(4):561-579.
[208] F. Peng, K. Tang, G. L. Chen, X. Yao. Population-based algorithm portfolios for numericaloptimization. IEEE Transactions on Evolutionary Computation[J],2010,14(5):782-800.
[209] P. K. Tripathi, S. Bandyopadhyay, S. K. Pal. Multi-objective particle swarm optimizationwith time variant inertia and acceleration coefficients. Information Sciences[J],2007,177(22):5033-5049.
[210] S. Das, P. N. Suganthan. Differential evolution: a survey of the state-of-the-art. IEEETransactions on Evolutionary Computation[J],2011,15(1):27-54.
[211] E. Mininno, F. Neri, F. Cupertino, D. Naso. Compact differential evolution. IEEETransactions on Evolutionary Computation[J],2011,15(1):32-54.
[212] B. Dorronsoro, P. Bouvry. Improving classical and decentralized differential evolution withnew mutation operator and population topologies. IEEE Transactions on EvolutionaryComputation[J],2011,15(1):67-98.
[213] M. G. Epitropakis, D. K. Tasoulis, N. G. Pavlidis, V. P. Plagianakos, M. N. Vrahatis.Enhancing differential evolution utilizing proximity-based mutation operators. IEEETransactions on Evolutionary Computation[J],2011,15(1):99-119.
[214] J. Lampinen, I. Zelinka. On stagnation of the differential evolution algorithm. The6thInternational Nendel Conference on Soft Computing[C],2000:76-83.
[215] R. G mperle, S. D. Müller, P. Koumoutsakos. A parameter study for differential evolution.Advances in Intelligent Systems, Fuzzy Systems, Evolutionary Computation[J],2002,10(1):293-298.
[216] S. Das, A. Konar, U. K. Chakraborty. Two improved differential evolution schemes forfaster global search. GECCO2005: Genetic and Evolutionary Computation Conference[C],2005:991-998.
[217] J. M. Whitacre, T. Q. Pham, R. A. Sarker. Credit assignment in adaptive evolutionaryalgorithms. The8th Annual Genetic and Evolutionary Computation Conference[C],2006:1353-1360.
[218] D. Thierens. An adaptive pursuit strategy for allocating operator probabilities. The Geneticand Evolutionary Computation Conference[C],2005:1546.
[219] L. N. De Castro, J. Timmis. An artificial immune network for multimodal functionoptimization. IEEE World Congress on Computational Intelligence[C],2002:699-704.
[220] E. Clark, A. Hone, J. Timmis. A markov chain model of the b-cell algorithm. ArtificialImmune Systems[J],2005,318-330.
[221] M. G. Gong, L. C. Jiao, L. N. Zhang, H. F. Du. Immune secondary response and clonalselection inspired optimizers. Progress in Natural Science[J],2009,19(2):237-253.
[222] E. K. Burke, M. R. Hyde, G. Kendall, G. Ochoa. Exploring hyper-heuristic methodologieswith genetic programming. Computational Intelligence[J],2009,1(3):177-201.
[223] P. Ross, J. G. Marfn-Blazquez. Constructive hyper-heuristics in class timetabling. IEEECongress on Evolutionary Computation[C],2005:1493-1500.
[224] E. K. Burke, G. Kendall, E. Soubeiga. A tabu-search hyperheuristic for timetabling androstering. Journal of Heuristics[J],2003,9(6):451-470.
[225] E. K. Burker, M. Hyde, G. Kendall, G. Ochoa, E. zcan, J. R. Woodward. A classificationof hyper-heuristic approaches. Handbook of Metaheuristics[J],2010,146(25):449-468.
[226] V. Cutello, G. Narzisi, G. Nicosia, M. Pavone. An immunological algorithm for globalnumerical optimization. The7th International Conference on Evolution Artificielle[C],2006:284-295.
[227] B. Parley, D. Scott. The Lanczos algorithm with selective orthogonalization[M].1979:217-238.
[228]M. Crouzeix, B. Philippe. The davidson method. SIAM Journal on Scientific Computing[J],1994,229(2):453-460.
[229]李湘清,孙秀霞,夏岩. Multi Agent动态分组模型用于多无人机协同任务分配.系统仿真学报[J],2010,22(05):1266-1269.
[230]朱毅,张涛,程农,宋靖雁.多UAV协同任务规划研究.系统仿真学报[J],2008,20(S2):194-199.
[231]叶媛媛.多UCAV协同任务规划方法研究,[博士学位论文].国防科学技术大学.2005.
[232]龙涛,朱华勇,沈林成.多UCAV协同中基于协商的分布式任务分配研究.宇航学报[J],2006,27(03):457-462.
[233]廖沫,陈宗基,周锐.基于MAS的多UAV协同任务分配设计与仿真.系统仿真学报[J],2007,19(10):2313-2317.
[234]宋敏,魏瑞轩,冯志明.基于差分进化算法的异构多无人机任务分配.系统仿真学报[J],2010,22(07):1706-1710.
[235]国博,王社伟,陶军.基于改进粒子群算法的多无人机任务分配研究.计算机仿真[J],2009,26(07):62-64+153.
[236]叶媛媛,闵春平,沈林成,朱华勇.基于混合遗传算法的多UCAV协同任务分配方法.东南大学学报(自然科学版)[J],2005,35(S2):53-58.
[237]周锐,吴雯漫,罗广文.自主多无人机的分散化协同控制.航空学报[J],2008,29(S1):26-32.
[238]金久才,张杰,官晟,王岩峰.自推进粒子群的空间同步平行编队控制.控制理论与应用[J],2011,28(04):587-590.
[239]李雪松,李颖晖,张水清,李霞,徐浩军.基于视线的无人机鲁棒自适应编队控制设计.飞行力学[J],2011,29(06):31-35.
[240]朱战霞,郑莉莉.无人机编队飞行控制器设计.飞行力学[J],2007,25(04):22-24.
[241]樊琼剑,杨忠,方挺,沈春林.多无人机协同编队飞行控制的研究现状.航空学报[J],2009,30(04):683-691.
[242]秦硕,朱凡,刘永学,麻忠文.多UAV协同路径规划研究.系统仿真学报[J],2008,20(23):6356-6358+6364.
[243]包勇,符小卫,高晓光.势场理论的多无人机协同路径规划方法.火力与指挥控制[J],2012,37(03):10-12.
[244]丁琳,高晓光,王健,王娜.针对突发威胁的无人机多机协同路径规划的方法.火力与指挥控制[J],2005,30(07):7-10.
[245] A. S. Manne. A target-assignment problem. Operations Research[J],1958,6(1):346–351.
[246] J. C. Braford, Determination of optimal assignment of a weapon system to severaltargets[R],1961.
[247] S. Matlin. A review of the literature on the missile-allocation problem. OperationsResearch[J],1970,18(2):334-373.
[248] O. Kwon, K. Lee, D. Kang, S. Park. A branch-and-price algorithm for a targeting problem.Naval Research Logistics[J],2007,54(7):732-741.
[249] G. K. E. Optimal resource allocation using genetic algorithms. Naval Review[J],1993,42(21):174–175.
[250] A. M. Madni, M. Andrecut. Efficient heuristic approaches to the weapon-target assignmentproblem. Journal of Aerospace Computing Information and Communication[J],2009,6(6):405-414.
[251] Y. X. Wang, L. J. Qian, Z. Guo, L. F. Ma. Weapon target assignment problem satisfyingexpected damage probabilities based on ant colony algorithm. Journal of SystemsEngineering and Electronics[J],2008,19(5):939-944.
[252] M. Z. Lee. Constrained weapon-target assignment: enhanced very large scale neighborhoodsearch algorithm. IEEE Transactions on Systems Man and Cybernetics Part A-Systems andHumans[J],2010,40(1):198-204.
[253] H. Cai, J. Liu, Y. Chen, H. Wang. Survey of the research on dynamic weapon-targetassignment problem. Journal of Systems Engineering and Electronics[J],2006,17(3):559-565.
[254] J. Chen, B. Xin, Z. H. Peng, L. H. Dou, J. Zhang. Evolutionary decision-makings for thedynamic weapon-target assignment problem. Science in China Series F-InformationSciences[J],2009,52(11):2006-2018.
[255] B. Xin, J. Chen, Z. H. Peng, L. H. Dou, J. Zhang. An efficient rule-based constructiveheuristic to solve dynamic weapon-target assignment problem. IEEE Transactions onSystems Man and Cybernetics Part A-Systems and Humans[J],2011,41(3):598-606.
[256]田菁,沈林成.多基地多无人机协同侦察问题研究.航空学报[J],2007,28(04):913-921.
[257]田菁.多无人机协同侦察任务规划问题建模与优化技术研究,[博士学位论文].国防科学技术大学.2007.
[258]王慧林,邱涤珊,黄小军,马满好,祝江汉.面向区域覆盖的电子侦察卫星规划方法研究.兵工学报[J],2011,32(11):1365-1372.
[259]彭辉.分布式多无人机协同区域搜索中的关键问题研究,[博士学位论文].国防科学技术大学.2009.
[260]李俨,董玉娜.基于SA-DPSO混合优化算法的协同空战火力分配.航空学报[J],2010,31(03):626-631.
[261]罗德林,段海滨,吴顺详,李茂青.基于启发式蚁群算法的协同多目标攻击空战决策研究.航空学报[J],2006,27(06):1166-1170.
[262]彭星光,高晓光,魏小丰.基于贝叶斯优化算法的UCAV编队对联合目标的协同攻击研究.系统仿真学报[J],2008,20(10):2693-2696.
[263]高晓光,王云辉,李波.基于综合效能的火力/电子战协同攻击决策研究.系统工程与电子技术[J],2007,29(12):2081-2084.
[264]蓝伟华,赵春玲.空对空多机协同攻击多个目标的战术决策研究.火力与指挥控制[J],1999,24(01):31-34.
[265]蓝伟华,赵春玲.空对空多机协同攻击多个目标战术决策研究.电光与控制[J],1999,01(01):1-5.
[266]盛蔚,李建勋,佟明安.微分对策论在多机协同攻击中的应用研究.系统工程与电子技术[J],1998,01(03):8-12.
[267]王磊.协同攻击任务规划认知演化计算研究,[博士学位论文].国防科学技术大学.2010.
[268]王磊,黄寒砚,吴红,杨峰,王维平.巡航导弹协同攻击体系目标仿真优化.系统仿真学报[J],2011,23(09):1866-1871.
[269]黄郡,单洪,满毅.基于区域覆盖的协同干扰任务分配模型及算法.兵工学报[J],2011,32(06):725-732.
[270] T. L. Saaty, L. G. Vargas. The seven pillars of the analytic hierarchy process. Models,Methods, Concepts&Applications of the Analytic Hierarchy Process[J],2001,23(08):27-46.
[271] Q. K. Pan, L. Wang, L. Gao, W. D. Li. An effective hybrid discrete differential evolutionalgorithm for the flow shop scheduling with intermediate buffers. Information Sciences[J],2011,181(3):668-685.
[272] L. Wang, Q. K. Pan, P. N. Suganthan, W. H. Wang, Y. M. Wang. A novel hybrid discretedifferential evolution algorithm for blocking flow shop scheduling problems. Computers&Operations Research[J],2010,37(3):509-520.
[273] Q. K. Pan, M. F. Tasgetiren, Y. C. Liang. A discrete differential evolution algorithm for thepermutation flowshop scheduling problem. Computers&Industrial Engineering[J],2008,55(4):795-816.
[274] D. B. Fogel. An introduction to simulated evolutionary optimization. IEEE Transactions onEvolutionary Computation[J],1994,5(1):3-14.
[275] T. L. Saaty. How to make a decision: the analytic hierarchy process. European Journal ofOperational Research[J],1990,48(1):9-26.
[276] T. L. Saaty. A scaling method for priorities in hierarchical structures. Journal ofMathematical Psychology[J],1977,15(3):234-281.
[277]林闯,胡杰,孔祥震.用户体验质量(QoE)的模型与评价方法综述.计算机学报[J],2012,35(01):1-15.
[278]刘川意,汪东升.基于HPCC和层次分析法的高性能计算系统评价模型.软件学报[J],2007,18(04):1039-1046.
[279] L. Mikhailov, P. Tsvetinov. Evaluation of services using a fuzzy analytic hierarchy process.Applied Soft Computing[J],2004,5(1):23-33.
[280]王文彬,孙其博,杨放春. MANET下环境感知的服务可用性量化评估模型.计算机研究与发展[J],2012,49(03):558-564.
[281]熊润群,罗军舟,宋爱波,金嘉晖.云计算环境下QoS偏好感知的副本选择策略.通信学报[J],2011,32(07):93-102.
[282] M. F. Tasgetiren, Q. K. Pan, Y. C. Liang, P. N. Suganthan. A discrete differential evolutionalgorithm for the total earliness and tardiness penalties with a common due date on asingle-machine. IEEE Symposium on Computational Intelligence in Scheduling[C],2007:271-278.
[283] Q. K. Pan, M. F. Tasgetiren, Y. C. Liang. Minimizing total earliness and tardiness penaltieswith a common due date on a single-machine using a discrete particle swarm optimizationalgorithm. Ant Colony Optimization and Swarm Intelligence[J],2006,4150(24):460-467.
[284] Y. Xue, Y. Zhuang, T. Q. Ni, S. R. Ni. One improved genetic algorithm applied in theproblem of dynamic jamming resource scheduling with multi-objective and multi-constraint.IEEE Fifth international conference on Bio-inspored Computing:Theories andApplications[C],2010:708-712.
[285] X. F. Zhai, Y. Zhuang. IIGA based algorithm for cooperative jamming resource allocation.Asia Pacific Conference on Postgraduate Research[C],2009:368-371.
[286] X. J. Bi, J. Xiao. Classification-based self-adaptive differential evolution with fast andreliable convergence performance. Soft Computing[J],2011,15(8):1581-1599.
[287] J. Brest, B. Boskovic, V. Zumer. An improved self-adaptive differential evolution algorithmin single objective constrained real-parameter optimization. IEEE Congress on EvolutionaryComputation[C],2010:1-8.
[288] J. Brest, M. S. Maucec. Self-adaptive differential evolution algorithm using population sizereduction and three strategies. Soft Computing[J],2011,15(11):2157-2174.
[289] F. Peng, K. Tang, G. L. Chen, X. Yao. Multi-start JADE with knowledge transfer fornumerical optimization.2009IEEE Congress on Evolutionary Computation[C],2009:1889-1895.
[290] Y. Wang, J. Z. Zhou, C. Zhou, Y. Q. Wang, H. Qin, Y. L. Lu. An improved self-adaptivePSO technique for short-term hydrothermal scheduling. Expert Systems with Applications[J],2012,39(3):2288-2295.
[291] S. Z. Zhao, P. N. Suganthan, S. Das. Self-adaptive differential evolution with modifiedmulti-trajectory search for CEC'2010large scale optimization. Swarm, Evolutionary, andMemetic Computing[C],2010:1-10.
[292] S. Z. Zhao, P. N. Suganthan, S. Das. Self-adaptive differential evolution withmulti-trajectory search for large-scale optimization. Soft Computing[J],2011,15(11):2175-2185.
[293] D. H. Wolpert, W. G. Macready. No free lunch theorems for optimization. IEEETransactions on Evolutionary Computation[J],1997,1(1):67-82.
[294] M. F. Tasgetiren, Q. K. Pan, P. N. Suganthan, Y. C. Liang. A discrete differential evolutionalgorithm for the no-wait flowshop scheduling problem with total flowtime criterion. IEEESymposium on Computational Intelligence in Scheduling[C],2007:251-258.
[295] L. Davis. Applying adaptive algorithms to epistatic domains. International Joint Conferenceon Artificial Intelligence[C],1985:161-163.
[296] G. D. E., L. R. Alleles, loci and the traveling salesman problem. International Conferenceon Genetic Algorithms and Their Applications[C],1985:151-159.
[297] Q. K. Pan, M. F. Tasgetiren, P. N. Suganthan, T. J. Chua. A discrete artificial bee colonyalgorithm for the lot-streaming flow shop scheduling problem. Information Sciences[J],2011,181(12):2455-2468.
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