仿生优化算法在平版印刷专色配色及油墨预置中的应用研究
|
摘要
近几年来,人工智能技术得到了长足的发展,特别是仿生优化算法更是不断涌现,仿生算法的提出大多是受自然界中生物群体行为的启发,这些优化算法的发展为复杂优化问题的求解提供了有效的解决渠道。本文针对目前使用比较广泛,同时也是比较典型的两种仿生优化方法(和声搜索优化算法和生物地理优化算法),分析两种算法各自的特点,引入局部搜寻机制,研究提高算法性能的方法与策略,并在此基础上提出相应的改进算法,用于解决平版印刷中的实际问题。总的来讲,本文主要的工作内容与相关研究成果可归纳如下：
(1)对仿生优化算法的框架模型进行了总结,给出了几种常用的改进策略和机制。在此基础上,以生物地理优化算法为例,提出了一种混合算法,该算法采用对立学习机制来产生较优初始群体,引入和声搜索算法中“调音”操作来提高其局部搜索能力,从而实现算法全局探索和局部开发能力的平衡,提高算法整体寻优能力。为验证所提混合算法的性能,将其应用于混沌系统的参数优化估计。仿真分析分别以典型的无时滞混沌系统和时滞混沌系统为对象,结果表明,在无噪声和含噪声条件下,所提混合算法可以得到比现有典型算法更好的参数估计结果,验证了所提算法的有效性和鲁棒性。
(2)研究分析了平版印刷中专色油墨配色算法存在的问题,提出一种基于光谱的专色油墨配色模型。针对该配色模型配方计算过程中存在的问题,将其转换为一带多约束复杂优化问题。在所提基于生物地理优化的混合算法基础上,进一步引入约束处理机制,用于解决该优化问题。实验通过随机选取已知配方的测试样本,与现有典型算法进行比较,结果表明,在所有测试样本中,所提算法均表现出更优的收敛性能。为了进一步验证所提专色油墨配色模型的性能,将其与目前使用较为广泛的FM Ⅲ配色软件进行比较,实验结果表明,在大部分测试样本中,所提模型能得到比FM Ⅲ配色软件精确更高的配方,相应色差值也更小,并且所得结果的色差值均在允许规定范围以内。
(3)在分析平版印刷工艺及相应输墨系统特点的基础上,提出了一种全局和声搜索优化的平版印刷油墨预置方法,该方法采用最小二乘支持向量机建立油墨预置问题中网点面积率与相应墨区墨键开度之间的非线性模型,并利用全局和声搜索算法对模型参数进行优化,继而用于解决平版印刷中的油墨预置问题。仿真实验结果表明,提出的全局和声搜索算法具有简单,执行速度快、优化能力强的特点,同时,将全局和声搜索算法应用于基于最小二乘支持向量的平版印刷油墨预置模型的参数优化,经与曼罗兰ROLAND700机型实际生产所得样本的测试比较,比该机自带功能具有更高的油墨预置精度。
The artificial intelligent technology is addressed a remarkable advance in recent years, especially, the bionic optimization algorithms. The proposing of these algorithms are mostly inspired by biological group behaviors in the nature, the development of the optimization algorithms provide an effective alternative for solving complex optimization problem. In this dissertation, two typical and widely used bionic optimization algorithms, namely, harmony search optimization algorithm and biogeography based optimization algorithm, are discussed. Based on the analysis of each algorithm, some local searching strategies are employed to improve its performance, and further applied to deal with some practical problems in offset printing. In general, the main works and contributions of this dissertation can be summarized as follows:
(1) The framework of bionic optimization algorithm model is summarized, and some improvement strategies and mechanisms are presented. Furthermore, the biogeography-based optimization (BBO) is employed as an example, and a BBO based hybrid algorithm is proposed. In this hybrid algorithm, the opposition based learning (OBL) approach is utilized to generate a better initial population, and the pitch adjusting mechanism in HS is employed to enhance the local searching ability. By fusing the operators in BBO with the OBL and pitch adjusting mechanisms, the exploration and exploitation capability may be enhanced and well balanced, and thus result in a more effective algorithm for global optimization. The proposed hybrid algorithm is further applied to the parameter estimation of chaotic systems to verify its performance. Numerical simulations are conducted on some typical chaotic systems with or without time-delay, the results show that, whatever the noise is concerned or not, the proposed scheme can obtain better estimated results than some typical existing algorithms, and the effectiveness and robustness of the proposed scheme are also demonstrated by the results.
(2) The problem in the spot color matching (SCM) algorithm is studied and analyzed, and a spectrophotometric based SCM model is proposed, and can be converted into a multiple constraints complex optimization problem for its feature. The aforementioned BBO based hybrid algorithm is further improved by employing a constraint handling method, and applied to solve this problem. The experiments are conducted on some randomly selected testing samples, and the comparisons between the proposed algorithm and some other typical methods show that, for all of the samples, the proposed algorithm exhibits better convergence performance. Further comparisons are conducted between the proposed SCM model and the widely used spot color matching FM III software, the experimental results show that, in most cases, the proposed model can achieve more accuracy color matching results than the FM III software in terms of color difference, and all of the color differences obtained by the proposed model, in fact, are lower to the acceptability limit.
(3) On the analysis of the offset printing technique and the corresponding ink system, a global harmony search optimization (GHSO) based ink preset method is proposed, and the nonlinear relationship model between the dot area rate and the corresponding ink key opening degree in each ink area is also established by the least squared support vector machine. The GHSO is used to optimize the parameters in the model, which is further applied to solve the ink preset problem of offset printing. The experimental results show that, the proposed GHSO has the features of easy complementation, fast running and strong optimization ability. The results of comparison with real printing data proved the validity of the proposed scheme, and the GHSO based ink preset model also has higher precision than the preset device of Roland printing machine.
(1)对仿生优化算法的框架模型进行了总结,给出了几种常用的改进策略和机制。在此基础上,以生物地理优化算法为例,提出了一种混合算法,该算法采用对立学习机制来产生较优初始群体,引入和声搜索算法中“调音”操作来提高其局部搜索能力,从而实现算法全局探索和局部开发能力的平衡,提高算法整体寻优能力。为验证所提混合算法的性能,将其应用于混沌系统的参数优化估计。仿真分析分别以典型的无时滞混沌系统和时滞混沌系统为对象,结果表明,在无噪声和含噪声条件下,所提混合算法可以得到比现有典型算法更好的参数估计结果,验证了所提算法的有效性和鲁棒性。
(2)研究分析了平版印刷中专色油墨配色算法存在的问题,提出一种基于光谱的专色油墨配色模型。针对该配色模型配方计算过程中存在的问题,将其转换为一带多约束复杂优化问题。在所提基于生物地理优化的混合算法基础上,进一步引入约束处理机制,用于解决该优化问题。实验通过随机选取已知配方的测试样本,与现有典型算法进行比较,结果表明,在所有测试样本中,所提算法均表现出更优的收敛性能。为了进一步验证所提专色油墨配色模型的性能,将其与目前使用较为广泛的FM Ⅲ配色软件进行比较,实验结果表明,在大部分测试样本中,所提模型能得到比FM Ⅲ配色软件精确更高的配方,相应色差值也更小,并且所得结果的色差值均在允许规定范围以内。
(3)在分析平版印刷工艺及相应输墨系统特点的基础上,提出了一种全局和声搜索优化的平版印刷油墨预置方法,该方法采用最小二乘支持向量机建立油墨预置问题中网点面积率与相应墨区墨键开度之间的非线性模型,并利用全局和声搜索算法对模型参数进行优化,继而用于解决平版印刷中的油墨预置问题。仿真实验结果表明,提出的全局和声搜索算法具有简单,执行速度快、优化能力强的特点,同时,将全局和声搜索算法应用于基于最小二乘支持向量的平版印刷油墨预置模型的参数优化,经与曼罗兰ROLAND700机型实际生产所得样本的测试比较,比该机自带功能具有更高的油墨预置精度。
The artificial intelligent technology is addressed a remarkable advance in recent years, especially, the bionic optimization algorithms. The proposing of these algorithms are mostly inspired by biological group behaviors in the nature, the development of the optimization algorithms provide an effective alternative for solving complex optimization problem. In this dissertation, two typical and widely used bionic optimization algorithms, namely, harmony search optimization algorithm and biogeography based optimization algorithm, are discussed. Based on the analysis of each algorithm, some local searching strategies are employed to improve its performance, and further applied to deal with some practical problems in offset printing. In general, the main works and contributions of this dissertation can be summarized as follows:
(1) The framework of bionic optimization algorithm model is summarized, and some improvement strategies and mechanisms are presented. Furthermore, the biogeography-based optimization (BBO) is employed as an example, and a BBO based hybrid algorithm is proposed. In this hybrid algorithm, the opposition based learning (OBL) approach is utilized to generate a better initial population, and the pitch adjusting mechanism in HS is employed to enhance the local searching ability. By fusing the operators in BBO with the OBL and pitch adjusting mechanisms, the exploration and exploitation capability may be enhanced and well balanced, and thus result in a more effective algorithm for global optimization. The proposed hybrid algorithm is further applied to the parameter estimation of chaotic systems to verify its performance. Numerical simulations are conducted on some typical chaotic systems with or without time-delay, the results show that, whatever the noise is concerned or not, the proposed scheme can obtain better estimated results than some typical existing algorithms, and the effectiveness and robustness of the proposed scheme are also demonstrated by the results.
(2) The problem in the spot color matching (SCM) algorithm is studied and analyzed, and a spectrophotometric based SCM model is proposed, and can be converted into a multiple constraints complex optimization problem for its feature. The aforementioned BBO based hybrid algorithm is further improved by employing a constraint handling method, and applied to solve this problem. The experiments are conducted on some randomly selected testing samples, and the comparisons between the proposed algorithm and some other typical methods show that, for all of the samples, the proposed algorithm exhibits better convergence performance. Further comparisons are conducted between the proposed SCM model and the widely used spot color matching FM III software, the experimental results show that, in most cases, the proposed model can achieve more accuracy color matching results than the FM III software in terms of color difference, and all of the color differences obtained by the proposed model, in fact, are lower to the acceptability limit.
(3) On the analysis of the offset printing technique and the corresponding ink system, a global harmony search optimization (GHSO) based ink preset method is proposed, and the nonlinear relationship model between the dot area rate and the corresponding ink key opening degree in each ink area is also established by the least squared support vector machine. The GHSO is used to optimize the parameters in the model, which is further applied to solve the ink preset problem of offset printing. The experimental results show that, the proposed GHSO has the features of easy complementation, fast running and strong optimization ability. The results of comparison with real printing data proved the validity of the proposed scheme, and the GHSO based ink preset model also has higher precision than the preset device of Roland printing machine.
引文
[1]J. H. Holland. Adaptation in Natural and Artificial Systems[M].Ann Arbor:University of Michigan Press, MI,1975.
[2]D. W. Corne, J. D. Knowles, M. J. Oates. The pareto envelope-based selection algorithm for multiobjective optimization[M]. Springer, Heidelberg,2000:839-848.
[3]D. Dasgupta. Artificial Immune Systems and their Applications[M], Springer, Heidelberg,1998:3-21.
[4]M. Dorigo, L. M. Gambardella. Ant colony system:a cooperative learning approach to the traveling salesman problem. IEEE Trans. on Evolutionary Computation,1997,1(1):53-66.
[5]R. Storn, K. Price. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. J. Global Optim.,1997,11(4):341-359.
[6]J. Kennedy, R.C. Eberhart. Particle swarm optimization[C]. Proceedings of the IEEE International Conference on Neural Networks. Perth,1995:1942-1948.
[7]D. Karaboga. An idea based on bee swarm for numerical optimization[R]. Kayseri, Turkey:Erciyes University,2005.
[8]Z. W. Geem, J. H. Kim, G. V. Loganathan. A new heuristic optimization algorithm:harmony search[J]. Simulation,2001,76(2):60-68.
[9]D. Simon. Biogeography-based optimization[J]. IEEE Transactions on Evolutionary Computation,2008, 6(12):702-713.
[10]C. H. Dai, W.R. Chen, Y.H. Song, et al. Seeker optimization algorithm:A novel stochastic search algorithm for global numerical optimization[J]. Journal of Systems Engineering and Electronics,2010, 21(2):300-311.
[11]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式：鱼群算法[J].系统工程理论与实践,2002,22(11)：33-39.
[12]K. M. Passino. Biomimicry of bacterial foraging for distributed optimization and control[J]. IEEE Control Systems Magazine,2002,22(3):52-67.
[13]K. N. KRISHNAND, D. GHOSE. Glowworm swarm optimisation:A new method for optimising multi-modal functions[J]. The International Journal of Computational Intelligence Studies,2009,1(1): 93-119.
[14]R. V. Rao, V. J. Savsani, D. P. Vakharia. Teaching-learning-based optimization:a novel method for constrained mechanical design optimization problems[J]. Computer-Aided Design,2011,43(3):303-315.
[15]A. H. Kashan. An efficient algorithm for constrained global optimization and application to mechanical engineering design:league championship algorithm (LCA)[J]. Computer-Aided Design,2011,43(12): 1769-1792.
[16]A. H. Gandomi, A. H. Alavi. Krill herd:A new bio-inspired optimization algorithm [J]. Communications in Nonlinear Science and Numerical Simulation,2012,17(12):4831-4845.
[17]Z. W. Geem, J. H. Kim, G. V. Loganathan. Harmony search optimization:application to pipe network design[J]. Int. J. Modell. Simulation,2002,22(2):125-133.
[18]M. T. Ayvaz. Application of Harmony Search algorithm to the solution of groundwater management models [J]. Advances in Water Resources,2009,32(6):916-924.K. S. Lee, Z. W. Geem. A new structural optimization method based on the harmony search algorithm[J]. Computers & Structures 2004, 82(9-10):781-798.
[20]K. S. Lee, Z. W. Geem, S. H. Lee, et al. The harmony search heuristic algorithm for discrete structural optimization[J]. Engineering Optimization,2005,37(7):663-684.
[21]K. S. Lee, Z. W. Geem. A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice[J]. Computer Methods in Applied Mechanics and Engineering,2005, 194(36-38):3902-3933.
[22]Z. W. Geem. Optimal cost design of water distribution networks using harmony search[J]. Engineering Optimization,2006,38(3),259-277.
[23]李亮,迟世春,林皋.引入和声策略的遗传算法在土坡非圆临界滑动面求解中的应用[J].水利学报,2005,36(8)：913-918.
[24]Z. W. Geem, H. Hwangbo. Application of harmony search to multi-objective optimization for satellite heat pipe design[C]. Proceedings of US-Korea Conference on Science, Technology, and Entrepreneurship, Teaneck, NJ, USA,2006, August 10-13.
[25]M. Mahdav, M. H. Chehreghani, H. Abolhassani, et al. Novel meta-heuristic algorithms for clustering web documents[J]. Applied Mathematics and Computation,2008,201(1-2):441-451.
[26]R. Forsati, A.T. Haghighat, M.Mahdavi. Harmony search based algorithms for bandwidth delay-constrained least-cost multicast routing[J]. Computer Communications,2008,31(10): 2505-2519.M. G. H. Omran, M. Mahdavi. Global-best harmony search[J]. Applied Mathematics and Computation,2008,198(2):643-656.
[28]L. S. Coelho, D. L. A. Bernert. A harmony search approach using exponential probability distribution applied to fuzzy logic control optimization[J]. Studies in Computational Intelligence,2010,270:77-88.
[29]1. M. Mahdav, M. Fesanghary, E. Damangir. An improved harmony search algorithm for solving optimization problems[J]. Applied Mathematics and Computation,2007,188(2):1567-157.
[30]M. Fesangharya, M. Mahdavib, M. M. Jolandanc, et al. Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems[J], Computer Methods in Applied Mechanics and Engineering,2008,197(33-40):3080-3091.
[31]D. Simon, M. Ergezer, D. Du, et al. Markov models for biogeography-based optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics,2011,41(1):299-306.
[32]D. Simon. A probabilistic analysis of a simplified biogeography-based optimization algorithm[J]. Evolutionary Computation,2011,19(2):167-188.
[33]D. Du, D. Simon, M. Ergezer. Biogeography-Based Optimization Combined with Evolutionary Strategy and Immigration Refusal[C]. IEEE Conference on Systems, Man, and Cybernetics, San Antonio, TX, 2009.10:1023-1028.
[34]S. Kumar, P. Bhalla, A. Singh. Fuzzy rule base generation from numerical data using biogeography-based optimization[J]. Journal of ET,2009,90(7):1-12.
[35]W. Y. Gong, Z. H. Cai, X. L. Charles. DE/BBO:A hybrid differential evolution with biogeography-based optimization for global numerical optimization[J]. Soft Computing,2010,15(4):645-665.
[36]V. K. Panchal, P. Singh, N. Kaur, et al. Biogeography based satellite image classification [J]. International Journal of Computer Science and Information Security,2009,6(2):269-274.
[37]N. K. Johal, S. Singh, H. Kundra. A hybrid FPAB/BBO Algorithm for Satellite Image classification[J]. International Journal of Computer Applications,2010,6(5):31-36.
[38]A. Chatterjee, P. Siarry, A. Nakib, et al. An improved biogeography based optimization approach for segmentation of human head CT-scan images employing fuzzy entropy [J]. Engineering Applications of Artificial Intelligence,2012,25(8):1698-1709.
[39]R. Rarick, D. Simon, F. V. Eugenio, et al. Biogeographybased optimization and the solution of the power flow problem[C]. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2009:1003-1008.
[40]H. Kundra, A. Kaur, V. K. Panchal. An integrated approach to biogeography based optimization with case based reasoning for retrieving groundwater possibility[C]. Map Asia, Singapore,8th Annual. Asian Conference and Exhibition on Geospatial Information, Technology and Applications,2009:18-20.
[41]A. Bhattacharya, P. K. Chattopadhyay. Solving complex economic load dispatch problems using biogeography-based optimization[J]. Expert Systems with Applications,2010,5(37):3605-3615.
[42]V. J. Savsani, R. V. Rao, D. P. Vakharia. Discrete optimisation of a gear train using biogeography based optimisation technique[J]. International Journal of Design Engineering,2009,2(2):205-223.
[43]D. Simon, M. Ergezer, D. Du. Population distributions in biogeography-based optimization algorithms with elitism[C]. San Antonio, TX, USA:Proceedings of the IEEE International Conference on Systems,Man, and Cybernetics,2009:991-996.
[44]M. Ergezer, D. Simon, D. Du. Oppositional biogeographybased optimization[C], IEEE International Conference on Systems Man and Cybernetics,2009:1009-1014.
[45]H. P. Ma, X. L. Chen. Equilibrium species counts and migration model tradeoffs for biogeography-based optimization[C].48th IEEE Conference on Decision and Control,2009:3306-3310.
[46]W. Y. Gong, Z. H. Cai, X. L. Charles. A real-coded biogeography-based optimization with mutation[J]. Applied Mathematics and Computation,2010,216(9):2749-2758.
[47]马海平,陈子栋,潘张鑫.一类基于物种迁移优化的进化算法[J].控制与决策,2009,24(11)：1620-1624.
[48]L. X. Tan, G. L. Guo. Quantum and biogeography based optimization for a class of combinatorial optimization[J]. Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation,2009:969-972.
[49]冯瑞乾.印刷概论[M].北京：石油工业出版社.1988.
[50]A. Karbasi, S. Moradian, S. Asiaban. Improving the performance of computer color matching procedures[J]. J Opt Soc Am A,2008,25(9):2251-2262.
[51]H. Zeng, N. Liao, H. Yu, et al. Calculation model used for recipe revision in computer color matching[J]. Optical Engineering,1999,38(12):1996-1999.
[52]R. S. Berns, M. Mohammadi. Single-constant simplification of kubelka-munk turbid-media theory for paint systems-a review[J]. Color Research and Application,2007,32(3):201-207.
[53]褚庭亮,庞多益,庞也驰等.复频谱油墨配色方法和系统[P].中国专利：101585257,2009-11-25.
[54]徐锦林,徐咏驰,徐军飞.基于色相角的专色油墨配色算法研究[J].中国印刷与包装研究,2011.4,3(2)：17-22.
[55]Helmut Kipphan. Handbook of print media-technologies and production methods[M]. Springer-Verlag Berlin Heidelberg,2001:332-334.
[56]L. Lehnberg. Ink Key Presetting in Offset Printing Press Using Digital Images of the Plates[D]. Sweden: Department of Science and Technology, Linkoping University,2002.
[57]范明,李华,杨静等.报纸印刷的油墨预置方法[P].中国专利：1939724,2007-04-04.
[58]N. Doherty, N. H. Durham. Ink Key Presetting System for Offset Printing Machines[P]. U. S. Pat: 6477954,2002-11-12.
[59]M. Werner, S. Gerd, S. Josef. Method and apparatus for ink control and zonal[P]. U.S.Pat:5170711, 1991-01-23.
[60]余节约,田培娟,项秀阳.一种胶印输出墨量预置的方法[P].中国专利：101844437A,2010-09-29.
[61]余节约.胶印油墨预置机制与技术的研究[D].南京：南京林业大学博士学位论文,2012.
[62]杨劲秋.智能优化算法评价模型研究[D].杭州：浙江大学博士学位论文,2011.
[63]J. F. Dupuis, Z. Fan, E. D. Goodman. Evolutionary Design of Both Topologies and Parameters of a Hybrid Dynamical System[J]. IEEE Transactions on Evolutionary Computation,2012.6,16(3):391-405.
[64]C. S. Li, J. Z. Zhou, X. Jian, et al. Parameters identification of chaotic system by chaotic gravitational search algorithm[J]. Chaos, Solitons & Fractals,2012,45(4):539-547.
[65]B. Alatas. Chaotic harmony search algorithms[J]. Applied Mathematics and Computation,2010,216(9): 2687-2699.
[66]L. Chuang, C. Hsiao, C. H. Yang. Chaotic particle swarm optimization for data clustering[J]. Expert Systems with Applications,2011,38(12):14555-14563.
[67]S. C. Leandro. Reliability-redundancy optimization by means of a chaotic differential evolution approach[J]. Chaos, Solitons & Fractals,2009,41(2):594-602.
[68]C. F. Xu, H. B. Duan, F. Liu. Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning[J]. Aerospace Science and Technology,2010,14(8):535-541.
[69]Y. He, J. Zhou, X. Xiang, et al. Comparison of different chaotic maps in particle swarm optimization algorithm for long-term cascaded hydroelectric system scheduling[J]. Solitons and Fractals,2009,42(5): 3169-3176.
[70]H. R. Tizhoosh. Opposition-based learning:A new scheme for machine intelligence[J]. International Conference on Computational Intelligence for Modelling Control and Automation, Vienna, Austria, November 28-30,2005:695-701.
[71]H. Wang, Z. Wu, S. Rahnamayan, et al. Enhancing particle swarm optimization using generalized opposition-based learning[J]. Information Sciences,2011,181(20):4699-4714.
[72]J. Wang, Z. Wu, H. Wang. Hybrid differential evolution algorithm with chaos and generalized opposition-based learning[J]. Advances in Computation and Intelligence,2010,6382:103-111.
[73]E. N. Alle, D. V. Michael Modeling genetic algorithms with Markov chains[J]. Annals of Mathematics and Artificial Intelligence,1992,5(1):79-88.
[74]J. A. Nelder, R. Mead. A simplex method for function minimization[J]. The Computer Journal,1965, 7(4):308-313.
[75]张勇,巩敦卫,张婉秋.一种基于单纯形法的改进微粒群优化算法及其收敛性分析[J].自动化学报,2009,35(3)：289-298.
[76]L. Wang, Y. Xu. An effective hybrid biogeography-based optimization algorithm for parameter estimation of chaotic systems. Expert Systems with Applications,2012,38(12):15103-15109.
[77]B. K.Panigrahi, V. R. Pandi. Bacterial foraging optimisation:Nelder-Mead hybrid algorithm for economic load dispatch[J]. IET Proceedings Generation Transmission and Distribution,2008,2(4): 556-565.
[78]L. Wang, Y. Xu, L. P. Li. Parameter identification of chaotic systems by hybrid Nelder-Mead simplex search and differential evolution algorithm[J].2011,38(4):3238-3245.
[79]P. K. Roy, S. P. Ghoshal, S. S. Thakur. Biogeography based optimization for multi-constraint optimal power flow with emission and non-smooth cost function[J]. Expert Systems with Applications,2011, 37(12),8221-8228.
[80]K. Jamuna, K. Swarup. Biogeography based optimization for optimal meter placement for security constrained state estimation[J]. Swarm and Evolutionary Computation,2011,1(2):89-96.
[81]E. N. Lorenz. Deterministic nonperiodic flow. J. Atoms Sci.,1963,20(1):130-141.
[82]王绍明.混沌系统控制及参数辨识的若干问题研究.武汉：华中科技大学博士学位论文,2007.
[83]A. Maybhate, R. Amritkar. Use of synchronization and adaptive control in parameter estimation from a time series[J]. Physical Review E,1999,59(1):284-293.
[84]P. Saha, S. Banerjee, A. R. Chowdhury. Chaos, signal communication and parameter estimation[J]. Physics Letters A,2004,326(1-2):133-139.
[85]D. L.Xu, F. F. Lu. An approach of parameter estimation for non-synchronous systems[J]. Chaos, Solitons & Fractals,2005,25(2):361-366.
[86]H. B. Fotsin, P. Woafo. Adaptive synchronization of a modified and uncertain chaotic Van der Pol-Duffing oscillator based on parameter identification[J]. Chaos, Solitons & Fractals,2005,24(5): 1363-1371.
[87]王开,裴文江,张毅峰,等.基于符号向量动力学的耦合映像格子参数估计[J].物理学报,2011,60(7)：070502.
[88]彭海朋,李丽香,杨义先,等.一阶时滞混沌的参数辨识[J].物理学报,2007,56(11)：6245-6249.
[89]C. Hua, X. Guan. Robust control of time-delay chaotic systems[J]. Physics Letters A,2003,314(1-2): 72-80.
[90]C. K. Cheng, H. H. Kuo, Y. Y. Hou, et al. Robust chaos synchronization of noise-perturbed chaotic systems with multiple time-delays[J]. Physica A:Statistical Mechanics and its Applications,2008, 387(13):3093-3102.
[91]T. Botmart, P. Niamsup, X. Liu. Synchronization of non-autonomous chaotic systems with time-varying delay via delayed feedback control[J]. Communications in Nonlinear Science and Numerical Simulation, 2012,17(4):1894-1907.
[92]R. Hegger, M. J. Bunner, H. Kantz. Identifying and modeling delay feedback systems[J]. Phys. Rev. Lett.,1998,81(3):558-561.
[93]J. Q. Lu, J. D. Cao. Synchronization-based approach for parameters identification in delayed chaotic neural networks[J]. Phys. A,2007,382(2):672-682.
[94]B. Rakshit, A. R. Chowdhury, P. Sana. Parameter estimation of a delay dynamical system using synchronization in presence of noise[J]. Chaos, Solitons & Fractals,2007,32(4),1278-1284.
[95]戴栋,马西奎,李富才,等.一种基于遗传算法的混沌系统参数估计方法[J].物理学报,2002,51(1)：2459-2462.
[96]Q. He, L. Wang, B. Liu. Parameter estimation for chaotic systems by particle swarm optimization[J]. Chaos, Solitons & Fractals,2007,34(2):654-661.
[97]Y. G. Tang, X. P. Guan. Parameter estimation for time-delay chaotic system by particle swarm optimization[J]. Chaos, Solitons & Fractals,2009,40(2):1391-1398.
[98]F. Gao, F. X. Fei, Q. Xu, et al. A novel artificial bee colony algorithm with space contraction for unknown parameters identification and time-delays of chaotic systems[J]. Applied Mathematics and Computation,2012,219(2):552-568.
[99]Y. G. Tang, M. Y. Cui, L. X. Li, et al. Parameter identification of time-delay chaotic system using chaotic ant swarm[J]. Chaos, Solitons & Fractals,2009,41(3):2097-2102.
[100]王钧炎,黄德先.基于混合差分进化算法的混沌系统参数估计[J].物理学报,2008,57(5)：2755-2759
[101]W. H. Ho, J. H. Chou, C. Y. Guo. Parameter identification of chaotic systems using improved differential evolution algorithm[J]. Nonlinear Dynamics,2010,61(1):29-41.
[102]Y. G. Tang, X. P. Guan. Parameter estimation of chaotic system with time-delay:A differential evolution approach[J]. Chaos, Solitons & Fractals,2009,42(5):3132-3139.
[103]P. Kubelka, F. Munk. Ein Beitrag zur Optik der Farbanstriche[J]. Zeit Tech Phys,1931,12:593-601.
[104]E. Walowit, C. J. McCarthy, R. S. Berns. Spectrophotometric color matching based on two-constant kubelka-munk theory[J]. Color Res Appl,1988,13(6):358-362.
[105]W. Cheetham, J. Graf. Case-based reasoning in color matching[J]. Case-Based Reasoning Research and Development,1997,1266:1-12.
[106]L. Yang, B. Kruse. Revised Kubelka-Munk theory. I. Theory and application[J]. J Opt Soc Am A 2004, 21(10):1933-1941.
[107]P. Edstrom. Examination of the revised Kubelka-Munk theory:considerations of modeling strategies [J]. J Opt Soc Am A,2007,24(2):548-556.
[108]S. S. Mikhail, S. S. Azer, W. M. Johnston. Accuracy of Kubelka-Munk reflectance theory for dental resin composite material[J]. Dental Materials,2012,28(7):729-735.
[109]B. S. Zhang, Y. Wang, L. Li. Optimization research of BP neural network with genetic algorithms used in textile color matching[J]. Computer Engineering and Design,2008,29(19):5033-5036.
[110]H. Q. Lv, B. S. Zhang. Application of particle swarm optimization algorithm based on RBF neural network in textile dyeing color matching[J]. Journal of Qingdao University (Engineering & Technology Edition),2009,24(3):10-24.
[111]M. M. Lana, M. Hogenkamp, R. B. M. Koehorst. Application of Kubelka-Munk analysis to the study of translucency in fresh-cut tomato[J]. Innovative Food Science and Emerging Technologies,2006,7(4): 302-308.
[112]B. Federica, M. Tiziano, R. Marcello. Color matching algorithms in ceramic tile production[J]. Journal of the European Ceramic Society,2006,26(3):311-316.
[113]B. S. Zhang, H. C. Li. Research on application for color matching in textile dyeing based on numerical analysis[C].2008 International Conference on Computer Science and Software Engineering, CSSE, 2008,1:357-360.
[114]D. Spitzer, R. Gottenbos, P. van Hensbergen, et al. A novel approach to color matching of automotive coatings[C]. Proceedings of the 21st International Conference in Organic Coatings Science and Technology,1996,29(1):235-238.
[115]K. T. Mehta, M. C. Bhavsar, P. M. Vora and H. S. Shah. Estimation of the Kubelka-Munk scattering coefficient from single particle scattering parameters[J]. Dyes and Pigments,1984,5(5):329-340.
[116]E. Allen. Basic equations used in computer color matching[J]. J. Opt. Soc. Am.,1966,56(9):1256-1257.
[117]P. H. Mcginnis. Spectrophotometric color matching with the least squares technique[J]. Color Engineering,1967,5(6):22-27.
[118]金福江,汤仪平.计算机配色中活性染料单位浓度K/S值研究[J].武汉理工大学学报(信息与管理工程版),2008,30(1)：83-92.
[119]J. L. Saunderson. Calculation of the Color of Pigmented Plastics[J]. J. Opt. Soc. Am.,1942,32(12): 727-729.
[120]徐海松.颜色技术原理及在印染中的应用(十六)[J].印染,2006,20(9)：36-38.
[121]B. Sluban. Comparison of colorimetric and spectrophotometric algorithms for computer match prediction[J]. Color Res Appl,1993,18(2):74-79.
[122]A. Homaifar, S. H. V. Lai, X. Qi. Constrained optimization via genetic algorithms[J]. Simulation,1994, 62(4):242-254.
[123]J. A. Joines, C.R. Houck. On the use of nonstationary penalty functions to solve nonlinear constrained optimization problems with GAs[C]. Proceedings of the International Conference on Evolutionary Computation, IEEE Press, Piscataway,1994:579-584.
[124]Z. Michalewicz, N. Attia, Evolutionary optimization of constrained problems[C]. Proceedings of the Third Annual Conference on Evolutionary Programming, World Scientific, Singapore,1994:98-108.
[125]K. Deb. An efficient constraint handling method for genetic algorithms[J]. Computer methods in applied mechanics and engineering,2000,186(2-4):311-338.
[126]S. Rahnamayan, H. R. Tizhoosh, M. M. A. Salama. Opposition-based differential evolution[J]. IEEE Transactions on Evolutionary Computation,2008,12(1):64-79.
[127]I. Boussaid, A. Chatterjee, P. Siarry, et al. Biogeography-based optimization for constrained optimization problems[J]. Computers & Operations Research,2012,39(12):3293-3304.
[128]C. Englund, A. Verikas. Ink feed control in a web-fed offset printing press[J]. International Journal of Advanced Manufacturing Technology.2008,39(9):919-930.
[129]C. Englund, A. Verikas. Combining traditional and neural-based techniques for ink feed control in a newspaper printing press[J]. Industrial Conference on Data Mining,2007,4597(8):214-227.
[130]J. A. K. Suykens, T. V. Gestel, J. D. Brabanter, et al. Least square support vector machines[M]. World Scientific, Singapore,2002:3-6.
[131]V. Vapnik. The nature of statistical learning theory[M]. NewYork:Springer-Verlag,1995.
[132]陈爱军.最小二乘支持向量机及其在工业过程建模中的应用[D].杭州：浙江大学博士学位论文,2006.
[133]M. G. Omran, M. Mahdavi. Global-best harmony search[J]. Applied Mathematics and Computation, 2008,198(2):643-656.
[134]C. M. Wang, Y. F. Huang. Self-adaptive harmony search algorithm for optimization[J]. Expert Systems with Applications,2010,37(4):2826-2837.
[135]孙丽萍,范宇,张冬妍,等.基于LSSVM的木材干燥在线建模研究[J].仪器仪表学报,2009,30(9)：1991-1995.
[136]吴建斌,田茂.基于支持向量机的地雷识别研究[J].仪器仪表学报,2009,30(7)：1487-1491.
[137]王焱,郭伟.基于最小二乘SVM的混沌跳频通信码预测方法[J].电子测量与仪器学报,2007,21(5)：64-68.
[138]C. L. D. Santos, B. D. L. Andrade. An improved harmony search algorithm for synchronization of discrete-time chaotic systems[J]. Chaos, Solitons & Fractals,2008,41(5):2526-2532.
[139]R. C. Eberhart, J. Kennedy. A new optimizer using particle swarm theory[C]. Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan,1995:39-43.
[2]D. W. Corne, J. D. Knowles, M. J. Oates. The pareto envelope-based selection algorithm for multiobjective optimization[M]. Springer, Heidelberg,2000:839-848.
[3]D. Dasgupta. Artificial Immune Systems and their Applications[M], Springer, Heidelberg,1998:3-21.
[4]M. Dorigo, L. M. Gambardella. Ant colony system:a cooperative learning approach to the traveling salesman problem. IEEE Trans. on Evolutionary Computation,1997,1(1):53-66.
[5]R. Storn, K. Price. Differential evolution-a simple and efficient heuristic for global optimization over continuous spaces[J]. J. Global Optim.,1997,11(4):341-359.
[6]J. Kennedy, R.C. Eberhart. Particle swarm optimization[C]. Proceedings of the IEEE International Conference on Neural Networks. Perth,1995:1942-1948.
[7]D. Karaboga. An idea based on bee swarm for numerical optimization[R]. Kayseri, Turkey:Erciyes University,2005.
[8]Z. W. Geem, J. H. Kim, G. V. Loganathan. A new heuristic optimization algorithm:harmony search[J]. Simulation,2001,76(2):60-68.
[9]D. Simon. Biogeography-based optimization[J]. IEEE Transactions on Evolutionary Computation,2008, 6(12):702-713.
[10]C. H. Dai, W.R. Chen, Y.H. Song, et al. Seeker optimization algorithm:A novel stochastic search algorithm for global numerical optimization[J]. Journal of Systems Engineering and Electronics,2010, 21(2):300-311.
[11]李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式：鱼群算法[J].系统工程理论与实践,2002,22(11)：33-39.
[12]K. M. Passino. Biomimicry of bacterial foraging for distributed optimization and control[J]. IEEE Control Systems Magazine,2002,22(3):52-67.
[13]K. N. KRISHNAND, D. GHOSE. Glowworm swarm optimisation:A new method for optimising multi-modal functions[J]. The International Journal of Computational Intelligence Studies,2009,1(1): 93-119.
[14]R. V. Rao, V. J. Savsani, D. P. Vakharia. Teaching-learning-based optimization:a novel method for constrained mechanical design optimization problems[J]. Computer-Aided Design,2011,43(3):303-315.
[15]A. H. Kashan. An efficient algorithm for constrained global optimization and application to mechanical engineering design:league championship algorithm (LCA)[J]. Computer-Aided Design,2011,43(12): 1769-1792.
[16]A. H. Gandomi, A. H. Alavi. Krill herd:A new bio-inspired optimization algorithm [J]. Communications in Nonlinear Science and Numerical Simulation,2012,17(12):4831-4845.
[17]Z. W. Geem, J. H. Kim, G. V. Loganathan. Harmony search optimization:application to pipe network design[J]. Int. J. Modell. Simulation,2002,22(2):125-133.
[18]M. T. Ayvaz. Application of Harmony Search algorithm to the solution of groundwater management models [J]. Advances in Water Resources,2009,32(6):916-924.K. S. Lee, Z. W. Geem. A new structural optimization method based on the harmony search algorithm[J]. Computers & Structures 2004, 82(9-10):781-798.
[20]K. S. Lee, Z. W. Geem, S. H. Lee, et al. The harmony search heuristic algorithm for discrete structural optimization[J]. Engineering Optimization,2005,37(7):663-684.
[21]K. S. Lee, Z. W. Geem. A new meta-heuristic algorithm for continuous engineering optimization: harmony search theory and practice[J]. Computer Methods in Applied Mechanics and Engineering,2005, 194(36-38):3902-3933.
[22]Z. W. Geem. Optimal cost design of water distribution networks using harmony search[J]. Engineering Optimization,2006,38(3),259-277.
[23]李亮,迟世春,林皋.引入和声策略的遗传算法在土坡非圆临界滑动面求解中的应用[J].水利学报,2005,36(8)：913-918.
[24]Z. W. Geem, H. Hwangbo. Application of harmony search to multi-objective optimization for satellite heat pipe design[C]. Proceedings of US-Korea Conference on Science, Technology, and Entrepreneurship, Teaneck, NJ, USA,2006, August 10-13.
[25]M. Mahdav, M. H. Chehreghani, H. Abolhassani, et al. Novel meta-heuristic algorithms for clustering web documents[J]. Applied Mathematics and Computation,2008,201(1-2):441-451.
[26]R. Forsati, A.T. Haghighat, M.Mahdavi. Harmony search based algorithms for bandwidth delay-constrained least-cost multicast routing[J]. Computer Communications,2008,31(10): 2505-2519.M. G. H. Omran, M. Mahdavi. Global-best harmony search[J]. Applied Mathematics and Computation,2008,198(2):643-656.
[28]L. S. Coelho, D. L. A. Bernert. A harmony search approach using exponential probability distribution applied to fuzzy logic control optimization[J]. Studies in Computational Intelligence,2010,270:77-88.
[29]1. M. Mahdav, M. Fesanghary, E. Damangir. An improved harmony search algorithm for solving optimization problems[J]. Applied Mathematics and Computation,2007,188(2):1567-157.
[30]M. Fesangharya, M. Mahdavib, M. M. Jolandanc, et al. Hybridizing harmony search algorithm with sequential quadratic programming for engineering optimization problems[J], Computer Methods in Applied Mechanics and Engineering,2008,197(33-40):3080-3091.
[31]D. Simon, M. Ergezer, D. Du, et al. Markov models for biogeography-based optimization[J]. IEEE Transactions on Systems, Man, and Cybernetics-Part B:Cybernetics,2011,41(1):299-306.
[32]D. Simon. A probabilistic analysis of a simplified biogeography-based optimization algorithm[J]. Evolutionary Computation,2011,19(2):167-188.
[33]D. Du, D. Simon, M. Ergezer. Biogeography-Based Optimization Combined with Evolutionary Strategy and Immigration Refusal[C]. IEEE Conference on Systems, Man, and Cybernetics, San Antonio, TX, 2009.10:1023-1028.
[34]S. Kumar, P. Bhalla, A. Singh. Fuzzy rule base generation from numerical data using biogeography-based optimization[J]. Journal of ET,2009,90(7):1-12.
[35]W. Y. Gong, Z. H. Cai, X. L. Charles. DE/BBO:A hybrid differential evolution with biogeography-based optimization for global numerical optimization[J]. Soft Computing,2010,15(4):645-665.
[36]V. K. Panchal, P. Singh, N. Kaur, et al. Biogeography based satellite image classification [J]. International Journal of Computer Science and Information Security,2009,6(2):269-274.
[37]N. K. Johal, S. Singh, H. Kundra. A hybrid FPAB/BBO Algorithm for Satellite Image classification[J]. International Journal of Computer Applications,2010,6(5):31-36.
[38]A. Chatterjee, P. Siarry, A. Nakib, et al. An improved biogeography based optimization approach for segmentation of human head CT-scan images employing fuzzy entropy [J]. Engineering Applications of Artificial Intelligence,2012,25(8):1698-1709.
[39]R. Rarick, D. Simon, F. V. Eugenio, et al. Biogeographybased optimization and the solution of the power flow problem[C]. Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, 2009:1003-1008.
[40]H. Kundra, A. Kaur, V. K. Panchal. An integrated approach to biogeography based optimization with case based reasoning for retrieving groundwater possibility[C]. Map Asia, Singapore,8th Annual. Asian Conference and Exhibition on Geospatial Information, Technology and Applications,2009:18-20.
[41]A. Bhattacharya, P. K. Chattopadhyay. Solving complex economic load dispatch problems using biogeography-based optimization[J]. Expert Systems with Applications,2010,5(37):3605-3615.
[42]V. J. Savsani, R. V. Rao, D. P. Vakharia. Discrete optimisation of a gear train using biogeography based optimisation technique[J]. International Journal of Design Engineering,2009,2(2):205-223.
[43]D. Simon, M. Ergezer, D. Du. Population distributions in biogeography-based optimization algorithms with elitism[C]. San Antonio, TX, USA:Proceedings of the IEEE International Conference on Systems,Man, and Cybernetics,2009:991-996.
[44]M. Ergezer, D. Simon, D. Du. Oppositional biogeographybased optimization[C], IEEE International Conference on Systems Man and Cybernetics,2009:1009-1014.
[45]H. P. Ma, X. L. Chen. Equilibrium species counts and migration model tradeoffs for biogeography-based optimization[C].48th IEEE Conference on Decision and Control,2009:3306-3310.
[46]W. Y. Gong, Z. H. Cai, X. L. Charles. A real-coded biogeography-based optimization with mutation[J]. Applied Mathematics and Computation,2010,216(9):2749-2758.
[47]马海平,陈子栋,潘张鑫.一类基于物种迁移优化的进化算法[J].控制与决策,2009,24(11)：1620-1624.
[48]L. X. Tan, G. L. Guo. Quantum and biogeography based optimization for a class of combinatorial optimization[J]. Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation,2009:969-972.
[49]冯瑞乾.印刷概论[M].北京：石油工业出版社.1988.
[50]A. Karbasi, S. Moradian, S. Asiaban. Improving the performance of computer color matching procedures[J]. J Opt Soc Am A,2008,25(9):2251-2262.
[51]H. Zeng, N. Liao, H. Yu, et al. Calculation model used for recipe revision in computer color matching[J]. Optical Engineering,1999,38(12):1996-1999.
[52]R. S. Berns, M. Mohammadi. Single-constant simplification of kubelka-munk turbid-media theory for paint systems-a review[J]. Color Research and Application,2007,32(3):201-207.
[53]褚庭亮,庞多益,庞也驰等.复频谱油墨配色方法和系统[P].中国专利：101585257,2009-11-25.
[54]徐锦林,徐咏驰,徐军飞.基于色相角的专色油墨配色算法研究[J].中国印刷与包装研究,2011.4,3(2)：17-22.
[55]Helmut Kipphan. Handbook of print media-technologies and production methods[M]. Springer-Verlag Berlin Heidelberg,2001:332-334.
[56]L. Lehnberg. Ink Key Presetting in Offset Printing Press Using Digital Images of the Plates[D]. Sweden: Department of Science and Technology, Linkoping University,2002.
[57]范明,李华,杨静等.报纸印刷的油墨预置方法[P].中国专利：1939724,2007-04-04.
[58]N. Doherty, N. H. Durham. Ink Key Presetting System for Offset Printing Machines[P]. U. S. Pat: 6477954,2002-11-12.
[59]M. Werner, S. Gerd, S. Josef. Method and apparatus for ink control and zonal[P]. U.S.Pat:5170711, 1991-01-23.
[60]余节约,田培娟,项秀阳.一种胶印输出墨量预置的方法[P].中国专利：101844437A,2010-09-29.
[61]余节约.胶印油墨预置机制与技术的研究[D].南京：南京林业大学博士学位论文,2012.
[62]杨劲秋.智能优化算法评价模型研究[D].杭州：浙江大学博士学位论文,2011.
[63]J. F. Dupuis, Z. Fan, E. D. Goodman. Evolutionary Design of Both Topologies and Parameters of a Hybrid Dynamical System[J]. IEEE Transactions on Evolutionary Computation,2012.6,16(3):391-405.
[64]C. S. Li, J. Z. Zhou, X. Jian, et al. Parameters identification of chaotic system by chaotic gravitational search algorithm[J]. Chaos, Solitons & Fractals,2012,45(4):539-547.
[65]B. Alatas. Chaotic harmony search algorithms[J]. Applied Mathematics and Computation,2010,216(9): 2687-2699.
[66]L. Chuang, C. Hsiao, C. H. Yang. Chaotic particle swarm optimization for data clustering[J]. Expert Systems with Applications,2011,38(12):14555-14563.
[67]S. C. Leandro. Reliability-redundancy optimization by means of a chaotic differential evolution approach[J]. Chaos, Solitons & Fractals,2009,41(2):594-602.
[68]C. F. Xu, H. B. Duan, F. Liu. Chaotic artificial bee colony approach to Uninhabited Combat Air Vehicle (UCAV) path planning[J]. Aerospace Science and Technology,2010,14(8):535-541.
[69]Y. He, J. Zhou, X. Xiang, et al. Comparison of different chaotic maps in particle swarm optimization algorithm for long-term cascaded hydroelectric system scheduling[J]. Solitons and Fractals,2009,42(5): 3169-3176.
[70]H. R. Tizhoosh. Opposition-based learning:A new scheme for machine intelligence[J]. International Conference on Computational Intelligence for Modelling Control and Automation, Vienna, Austria, November 28-30,2005:695-701.
[71]H. Wang, Z. Wu, S. Rahnamayan, et al. Enhancing particle swarm optimization using generalized opposition-based learning[J]. Information Sciences,2011,181(20):4699-4714.
[72]J. Wang, Z. Wu, H. Wang. Hybrid differential evolution algorithm with chaos and generalized opposition-based learning[J]. Advances in Computation and Intelligence,2010,6382:103-111.
[73]E. N. Alle, D. V. Michael Modeling genetic algorithms with Markov chains[J]. Annals of Mathematics and Artificial Intelligence,1992,5(1):79-88.
[74]J. A. Nelder, R. Mead. A simplex method for function minimization[J]. The Computer Journal,1965, 7(4):308-313.
[75]张勇,巩敦卫,张婉秋.一种基于单纯形法的改进微粒群优化算法及其收敛性分析[J].自动化学报,2009,35(3)：289-298.
[76]L. Wang, Y. Xu. An effective hybrid biogeography-based optimization algorithm for parameter estimation of chaotic systems. Expert Systems with Applications,2012,38(12):15103-15109.
[77]B. K.Panigrahi, V. R. Pandi. Bacterial foraging optimisation:Nelder-Mead hybrid algorithm for economic load dispatch[J]. IET Proceedings Generation Transmission and Distribution,2008,2(4): 556-565.
[78]L. Wang, Y. Xu, L. P. Li. Parameter identification of chaotic systems by hybrid Nelder-Mead simplex search and differential evolution algorithm[J].2011,38(4):3238-3245.
[79]P. K. Roy, S. P. Ghoshal, S. S. Thakur. Biogeography based optimization for multi-constraint optimal power flow with emission and non-smooth cost function[J]. Expert Systems with Applications,2011, 37(12),8221-8228.
[80]K. Jamuna, K. Swarup. Biogeography based optimization for optimal meter placement for security constrained state estimation[J]. Swarm and Evolutionary Computation,2011,1(2):89-96.
[81]E. N. Lorenz. Deterministic nonperiodic flow. J. Atoms Sci.,1963,20(1):130-141.
[82]王绍明.混沌系统控制及参数辨识的若干问题研究.武汉：华中科技大学博士学位论文,2007.
[83]A. Maybhate, R. Amritkar. Use of synchronization and adaptive control in parameter estimation from a time series[J]. Physical Review E,1999,59(1):284-293.
[84]P. Saha, S. Banerjee, A. R. Chowdhury. Chaos, signal communication and parameter estimation[J]. Physics Letters A,2004,326(1-2):133-139.
[85]D. L.Xu, F. F. Lu. An approach of parameter estimation for non-synchronous systems[J]. Chaos, Solitons & Fractals,2005,25(2):361-366.
[86]H. B. Fotsin, P. Woafo. Adaptive synchronization of a modified and uncertain chaotic Van der Pol-Duffing oscillator based on parameter identification[J]. Chaos, Solitons & Fractals,2005,24(5): 1363-1371.
[87]王开,裴文江,张毅峰,等.基于符号向量动力学的耦合映像格子参数估计[J].物理学报,2011,60(7)：070502.
[88]彭海朋,李丽香,杨义先,等.一阶时滞混沌的参数辨识[J].物理学报,2007,56(11)：6245-6249.
[89]C. Hua, X. Guan. Robust control of time-delay chaotic systems[J]. Physics Letters A,2003,314(1-2): 72-80.
[90]C. K. Cheng, H. H. Kuo, Y. Y. Hou, et al. Robust chaos synchronization of noise-perturbed chaotic systems with multiple time-delays[J]. Physica A:Statistical Mechanics and its Applications,2008, 387(13):3093-3102.
[91]T. Botmart, P. Niamsup, X. Liu. Synchronization of non-autonomous chaotic systems with time-varying delay via delayed feedback control[J]. Communications in Nonlinear Science and Numerical Simulation, 2012,17(4):1894-1907.
[92]R. Hegger, M. J. Bunner, H. Kantz. Identifying and modeling delay feedback systems[J]. Phys. Rev. Lett.,1998,81(3):558-561.
[93]J. Q. Lu, J. D. Cao. Synchronization-based approach for parameters identification in delayed chaotic neural networks[J]. Phys. A,2007,382(2):672-682.
[94]B. Rakshit, A. R. Chowdhury, P. Sana. Parameter estimation of a delay dynamical system using synchronization in presence of noise[J]. Chaos, Solitons & Fractals,2007,32(4),1278-1284.
[95]戴栋,马西奎,李富才,等.一种基于遗传算法的混沌系统参数估计方法[J].物理学报,2002,51(1)：2459-2462.
[96]Q. He, L. Wang, B. Liu. Parameter estimation for chaotic systems by particle swarm optimization[J]. Chaos, Solitons & Fractals,2007,34(2):654-661.
[97]Y. G. Tang, X. P. Guan. Parameter estimation for time-delay chaotic system by particle swarm optimization[J]. Chaos, Solitons & Fractals,2009,40(2):1391-1398.
[98]F. Gao, F. X. Fei, Q. Xu, et al. A novel artificial bee colony algorithm with space contraction for unknown parameters identification and time-delays of chaotic systems[J]. Applied Mathematics and Computation,2012,219(2):552-568.
[99]Y. G. Tang, M. Y. Cui, L. X. Li, et al. Parameter identification of time-delay chaotic system using chaotic ant swarm[J]. Chaos, Solitons & Fractals,2009,41(3):2097-2102.
[100]王钧炎,黄德先.基于混合差分进化算法的混沌系统参数估计[J].物理学报,2008,57(5)：2755-2759
[101]W. H. Ho, J. H. Chou, C. Y. Guo. Parameter identification of chaotic systems using improved differential evolution algorithm[J]. Nonlinear Dynamics,2010,61(1):29-41.
[102]Y. G. Tang, X. P. Guan. Parameter estimation of chaotic system with time-delay:A differential evolution approach[J]. Chaos, Solitons & Fractals,2009,42(5):3132-3139.
[103]P. Kubelka, F. Munk. Ein Beitrag zur Optik der Farbanstriche[J]. Zeit Tech Phys,1931,12:593-601.
[104]E. Walowit, C. J. McCarthy, R. S. Berns. Spectrophotometric color matching based on two-constant kubelka-munk theory[J]. Color Res Appl,1988,13(6):358-362.
[105]W. Cheetham, J. Graf. Case-based reasoning in color matching[J]. Case-Based Reasoning Research and Development,1997,1266:1-12.
[106]L. Yang, B. Kruse. Revised Kubelka-Munk theory. I. Theory and application[J]. J Opt Soc Am A 2004, 21(10):1933-1941.
[107]P. Edstrom. Examination of the revised Kubelka-Munk theory:considerations of modeling strategies [J]. J Opt Soc Am A,2007,24(2):548-556.
[108]S. S. Mikhail, S. S. Azer, W. M. Johnston. Accuracy of Kubelka-Munk reflectance theory for dental resin composite material[J]. Dental Materials,2012,28(7):729-735.
[109]B. S. Zhang, Y. Wang, L. Li. Optimization research of BP neural network with genetic algorithms used in textile color matching[J]. Computer Engineering and Design,2008,29(19):5033-5036.
[110]H. Q. Lv, B. S. Zhang. Application of particle swarm optimization algorithm based on RBF neural network in textile dyeing color matching[J]. Journal of Qingdao University (Engineering & Technology Edition),2009,24(3):10-24.
[111]M. M. Lana, M. Hogenkamp, R. B. M. Koehorst. Application of Kubelka-Munk analysis to the study of translucency in fresh-cut tomato[J]. Innovative Food Science and Emerging Technologies,2006,7(4): 302-308.
[112]B. Federica, M. Tiziano, R. Marcello. Color matching algorithms in ceramic tile production[J]. Journal of the European Ceramic Society,2006,26(3):311-316.
[113]B. S. Zhang, H. C. Li. Research on application for color matching in textile dyeing based on numerical analysis[C].2008 International Conference on Computer Science and Software Engineering, CSSE, 2008,1:357-360.
[114]D. Spitzer, R. Gottenbos, P. van Hensbergen, et al. A novel approach to color matching of automotive coatings[C]. Proceedings of the 21st International Conference in Organic Coatings Science and Technology,1996,29(1):235-238.
[115]K. T. Mehta, M. C. Bhavsar, P. M. Vora and H. S. Shah. Estimation of the Kubelka-Munk scattering coefficient from single particle scattering parameters[J]. Dyes and Pigments,1984,5(5):329-340.
[116]E. Allen. Basic equations used in computer color matching[J]. J. Opt. Soc. Am.,1966,56(9):1256-1257.
[117]P. H. Mcginnis. Spectrophotometric color matching with the least squares technique[J]. Color Engineering,1967,5(6):22-27.
[118]金福江,汤仪平.计算机配色中活性染料单位浓度K/S值研究[J].武汉理工大学学报(信息与管理工程版),2008,30(1)：83-92.
[119]J. L. Saunderson. Calculation of the Color of Pigmented Plastics[J]. J. Opt. Soc. Am.,1942,32(12): 727-729.
[120]徐海松.颜色技术原理及在印染中的应用(十六)[J].印染,2006,20(9)：36-38.
[121]B. Sluban. Comparison of colorimetric and spectrophotometric algorithms for computer match prediction[J]. Color Res Appl,1993,18(2):74-79.
[122]A. Homaifar, S. H. V. Lai, X. Qi. Constrained optimization via genetic algorithms[J]. Simulation,1994, 62(4):242-254.
[123]J. A. Joines, C.R. Houck. On the use of nonstationary penalty functions to solve nonlinear constrained optimization problems with GAs[C]. Proceedings of the International Conference on Evolutionary Computation, IEEE Press, Piscataway,1994:579-584.
[124]Z. Michalewicz, N. Attia, Evolutionary optimization of constrained problems[C]. Proceedings of the Third Annual Conference on Evolutionary Programming, World Scientific, Singapore,1994:98-108.
[125]K. Deb. An efficient constraint handling method for genetic algorithms[J]. Computer methods in applied mechanics and engineering,2000,186(2-4):311-338.
[126]S. Rahnamayan, H. R. Tizhoosh, M. M. A. Salama. Opposition-based differential evolution[J]. IEEE Transactions on Evolutionary Computation,2008,12(1):64-79.
[127]I. Boussaid, A. Chatterjee, P. Siarry, et al. Biogeography-based optimization for constrained optimization problems[J]. Computers & Operations Research,2012,39(12):3293-3304.
[128]C. Englund, A. Verikas. Ink feed control in a web-fed offset printing press[J]. International Journal of Advanced Manufacturing Technology.2008,39(9):919-930.
[129]C. Englund, A. Verikas. Combining traditional and neural-based techniques for ink feed control in a newspaper printing press[J]. Industrial Conference on Data Mining,2007,4597(8):214-227.
[130]J. A. K. Suykens, T. V. Gestel, J. D. Brabanter, et al. Least square support vector machines[M]. World Scientific, Singapore,2002:3-6.
[131]V. Vapnik. The nature of statistical learning theory[M]. NewYork:Springer-Verlag,1995.
[132]陈爱军.最小二乘支持向量机及其在工业过程建模中的应用[D].杭州：浙江大学博士学位论文,2006.
[133]M. G. Omran, M. Mahdavi. Global-best harmony search[J]. Applied Mathematics and Computation, 2008,198(2):643-656.
[134]C. M. Wang, Y. F. Huang. Self-adaptive harmony search algorithm for optimization[J]. Expert Systems with Applications,2010,37(4):2826-2837.
[135]孙丽萍,范宇,张冬妍,等.基于LSSVM的木材干燥在线建模研究[J].仪器仪表学报,2009,30(9)：1991-1995.
[136]吴建斌,田茂.基于支持向量机的地雷识别研究[J].仪器仪表学报,2009,30(7)：1487-1491.
[137]王焱,郭伟.基于最小二乘SVM的混沌跳频通信码预测方法[J].电子测量与仪器学报,2007,21(5)：64-68.
[138]C. L. D. Santos, B. D. L. Andrade. An improved harmony search algorithm for synchronization of discrete-time chaotic systems[J]. Chaos, Solitons & Fractals,2008,41(5):2526-2532.
[139]R. C. Eberhart, J. Kennedy. A new optimizer using particle swarm theory[C]. Proceedings of the Sixth International Symposium on Micromachine and Human Science, Nagoya, Japan,1995:39-43.