[1]张伟伟,高奎,张卫正,等.基于成功历史自适应的混合克隆选择算法[J].郑州大学学报(工学版),2019,40(02):26-31.
 An Improve Particle Swarm Optimization Algorithm Based on Learning Theory[J].Journal of Zhengzhou University (Engineering Science),2019,40(02):26-31.
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基于成功历史自适应的混合克隆选择算法()
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《郑州大学学报(工学版)》[ISSN:1671-6833/CN:41-1339/T]

卷:
40
期数:
2019年02期
页码:
26-31
栏目:
出版日期:
2019-03-19

文章信息/Info

Title:
An Improve Particle Swarm Optimization Algorithm Based on Learning Theory
作者:
张伟伟高奎张卫正孟颍辉王华张秋闻
文献标志码:
A
摘要:
针对传统克隆选择算法在求解复杂最优化问题时遇到的早熟收敛和易陷入局部最优的缺点提出了基于成功历史自适应的混合克隆选择算法.该算法引入改进的基因重组策略来加强算法的全局搜索能力,并将成功历史自适应变异算子与超变异算子相结合提出了成功历史自适应超变异算子来提升算法的性能.在25个测试函数上测试了算法的性能,实验结果表明所提出的算法能够有效提升传统克隆选择算法的性能,对比其他的算法具备很强的竞争力.
Abstract:
Since PSO algorithm was casy to get trapped into local optimum ,in this paper ,based on the learning theory a new PSO algorithm nameed as L-PSO was proposed. In L-PSO,an inger value was set as the maximum cycle limitation for the global best particles ,and propose a clustering grouping mutation mechanism which could devide the particles into some subb-swarms and generates the competitive by crossover and mutation of the teo centers selected randomly from sub-swarms.Then the competitive particle was used to replace the global optimum particle which could help jump oout of the local optimum and improve theconvergence speed. Experimenta results on several benchmark test function shoxed that L-PSO was very effective. 
更新日期/Last Update: 2019-03-24