蚁群算法研究与应用的新进展
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摘要
蚁群算法是一种源于大自然生物界的仿生进化算法,具有自组织性、正反馈性、较强的鲁棒性和分布式计算等特性,且易于与其它算法相结合,在众多的复杂组合优化领域中有着广阔的应用前景。首先对蚁群算法的理论及其重要参数进行了阐述,继而分析了其在参数优化和智能融合方面的改进与应用;然后对其在车间作业调度问题、车辆路径问题、图像处理、电力系统优化等领域的应用进展进行了综述;最后对其理论研究和应用领域可能存在的问题及对策进行了探讨和展望。
The ant colony algorithm is a bionic evolutionary algorithm derived from the natural biological world.It has the characteristics of self-organization,positive feedback,strong robustness,and distributed computing,and it is easy to combine with other algorithms.it is therefore of great applied value in the complex combinatorial optimization field.We firstly introduce the theory of the ant colony algorithm and its important parameters.Then we analyze the improvement and applications in parameter optimization and intelligent fusion.Thirdly,we summarize the progress of applications in job-shop scheduling problem,vehicle routing problem,image processing and electric power system optimization.Finally,we discuss the potential problems in research work in theory and application domain,as well as some possible countermeasures.
The ant colony algorithm is a bionic evolutionary algorithm derived from the natural biological world.It has the characteristics of self-organization,positive feedback,strong robustness,and distributed computing,and it is easy to combine with other algorithms.it is therefore of great applied value in the complex combinatorial optimization field.We firstly introduce the theory of the ant colony algorithm and its important parameters.Then we analyze the improvement and applications in parameter optimization and intelligent fusion.Thirdly,we summarize the progress of applications in job-shop scheduling problem,vehicle routing problem,image processing and electric power system optimization.Finally,we discuss the potential problems in research work in theory and application domain,as well as some possible countermeasures.
引文
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[8]Juang C F,Chang P H.Recurrent fuzzy system design using elite-guided continuous ant colony optimization[J].Applied Soft Computing,2011,11(2):2687-2697.
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[10]Liu Guo-bao,Zhang Jie.Dynamic schedule method based on improved rolling time domain optimization strategy[J].Journal of Mechanical Engineering,2013,49(14):182-190.(in Chinese)
[11]Li M P,Yao M.An optimized ant system for clustering with elitist ant and local search[C]∥Proc of ITM Web of Conferences,2016:Article No.05011.
[12]Yousefikhoshbakht M,Didehvar F,Rahmati F.A combination of modified tabu search and elite ant system to solve the vehicle routing problem with simultaneous pickup and delivery[J].Journal of Industrial and Production Engineersing,2014,31(2):65-75.
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[14]Bai J,Yang G K,Chen Y W,et al.A model induced max-min ant colony optimization for asymmetric traveling salesman problem[J].Applied Soft Computing,2013,13(3):1365-1375.
[15]AI-Shihabi S.A hybrid of max-min ant system and linear programming for the k-covering problem[J].Computers&Operations Research,2016,76:1-11.
[16]AI-Shihabi S T,Aldurgam M M.A max-min ant system for the finance-based scheduling problem[J].Computers&Industrial Engineering,2017,110:264-276.
[17]Sujaree K,Kitjaruwankul S,Boonamnaj P,et al.Transmembrane helix assembly by max-min ant system algorithm[J].Chemical Biology&Drug Design,2015,86(6):1360-1372.
[18]Parsa N R,Karimi B,Husseini S M M.Minimizing total flow time on a batch processing machine using a hybrid max-min ant system[J].Computers&Industrial Engineering,2016,99(C):372-381.
[19]Gambardella L M,Dorigo M.Solving symmetric and asymmetric TSPs by ant colonies[C]∥Proc of IEEE International Conference on Evolutionary Computation,1996:622-627.
[20]Wang Fang,Li Mei-an,Duan Wei-jun.Cloud computing task scheduling based on dynamically adaptive ant colony algorithm[J].Journal of Computer Applications,2013,33(11):3160-3162.(in Chinese)
[21]Zhang C T,Zhao A X.Using adaptive ant colony algorithm optimized BP neural network to identify the DGA fault[C]∥Proc of 2013IEEE International Conference on Region 10(Tencon 2013),2013:1-4.
[22]Lei X S,Guo K X.The model identification for small unmanned aerial rotorcraft based on adaptive ant colony algorithm[J].Journal of Bionic Engineering,2012,9(4):508-514.
[23]Lin Y,Gong Y J,Zhang J.An adaptive ant colony optimization algorithm for constructing cognitive diagnosis tests[J].Applied Soft Computing,2017,52:1-13.
[24]Yang Q,Chen W N,Yu Z,et al.Adaptive multimodal continuous ant colony optimization[J].IEEE Transactions on Evolutionary Computation,2017,21(2):191-205.
[25]Ding Jian-li,Chen Zeng-qiang,Yuan Zhu-zhi.On the combination of genetic algorithm and ant algorithm[J].Journal of Computer Research and Development,2003,40(9):1351-1356.(in Chinese)
[26]Ciornei I,Kyriakides E.Hybrid ant colony-genetic algorithm(GAAPI)for global continuous optimization[J].IEEETransactions on Systems Man&Cybernetics Part B Cybernetics,2012,42(1):234-45.
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[29]Cao Qing-kui,Zhao Fei.Port trucks route optimization based on GA-ACO[J].Systems Engineering-Theory&Practice,2013,33(7):1820-1828.(in Chinese)
[30]Dai Y V,Lou Y S,Lu X.A task scheduling algorithm based on genetic algorithm and ant colony optimization algorithm with multi-QoS constraints in cloud computing[C]∥Proc of the 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics,2015:428-431.
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