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改进的人口迁移算法 被引量:2

Improved population migration algorithm
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摘要 为了提高人口迁移算法(PMA)的全局优化能力,受达尔文的"优胜劣汰"理论及蜜蜂繁殖进化机制的启发,针对PMA的不足对算法进行了改进,多个经典函数的实验仿真表明改进算法的有效可行性。 Inspired both by the mechanism of the evolution of honeybee and the theory of survival of the fittest,an improved population migration algorithm to make a better efficient of PMA is proposed in this paper.The experiment shows that the improve algorithm is more efficient.
出处 《计算机工程与应用》 CSCD 北大核心 2008年第33期218-220,共3页 Computer Engineering and Applications
基金 国家自然科学基金No.60461001 广西省自然科学基金No.0542048 广西民族大学研究生教育创新计划项目(No.gxun-chx0755) 梧州学院青年基金项目(No.2007D007)。~~
关键词 人口迁移算法 优胜劣汰 最优保留 竞争 全局收敛性 PMA(population migration algorithm) survival of the fittest elitist preserved compete global convergence
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  • 1鄢烈祥,麻德贤.连续变量函数全局优化算法—列队竞争算法[J].应用基础与工程科学学报,1999,7(2):215-221. 被引量:2
  • 2杨俊杰,周建中,喻菁,吴玮.基于混沌搜索的粒子群优化算法[J].计算机工程与应用,2005,41(16):69-71. 被引量:46
  • 3Da-Qing Guo,Yong-Jin Zhao,Hui Xiong,Xiao Li.A New Class of Hybrid Particle Swarm Optimization Algorithm[J].Journal of Electronic Science and Technology of China,2007,5(2):149-152. 被引量:3
  • 4李振涛,张国立,王淑玲.人口迁移神经网络在电力系统短期负荷预测中的应用[J].华东电力,2007,35(7):17-20. 被引量:4
  • 5Krishnanand K N, Ghose D.Glowworm swarm optimisation: a new method for optimising multi-modal functions[J].Int J Com- putational Intelligence Studies, 2009,1 ( 1 ) : 93-119.
  • 6Deep K,Bansal J C.Mean partical swarm optimization for function optimization[J].int J Computational Intelligence Studies,2009,1 (1):72-92.
  • 7ZHANG Wei-wei, LUO Qi-fang, ZHOU Yong-quan. A method for training RBF neural networks based on population migration algorithm [ C ]//Proc of International Conference on Artificial Intelligence and Computational Intelligence. Washington DC : IEEE Computer Society, 2009 : 165-169.
  • 8SU Sheng. Image classification based on particle swarm optimization combined with K-means [ C ]//Proe of International Conference on Test and Measurement. 2009:367-370.
  • 9ZHANG Ji-hui, XU Jun-qin. Evolutionary programming based on uniform design with application to multiobjeetive optimization [ C ]//Proc of the 5th World Congress on Iotelligent Control and Automation. 2004:2298- 2302.
  • 10KHOA T Q D, NAKAGAWA M. Training muhilayer neural network by global chaos optimization algorithms [ C ]//Proc of International Joint Conference on Neural Networks. 2007 : 136-141.

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