| 自适应变异综合学习粒子群优化算法 |
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| 引用本文： | 蔡昭权,黄翰.自适应变异综合学习粒子群优化算法[J].计算机工程,2009,35(7):170-171,202. |
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| 作者姓名： | 蔡昭权 黄翰 |
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| 作者单位： | 蔡昭权,CAI Zhao-quan(惠州学院网络中心,惠州,516007);黄翰,HUANG Han(华南理工大学软件学院,广州,510006)
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| 摘 要： | 针对以往粒子群优化算法多样性差且易局部收敛的不足,提出改进综合学习粒子群优化(CLPSO)算法的最小方差优先自适应变异策略,设计自适应变异综合粒子群优化(CLPSO-M)算法.多个标准测试问题的对比实验数据表明,CLPSO-M算法比CLPSO算法的全局搜索能力更强,求解效果更稳定.
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| 关 键 词： | 群体智能 粒子群优化算法 综合学习 最小方差优先 自适应变异 |
| 修稿时间： | |
Comprehensive Learning Particle Swarm Optimization Algorithm with Adaptive Mutation |
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| CAI Zhao-quan,HUANG Han.Comprehensive Learning Particle Swarm Optimization Algorithm with Adaptive Mutation[J].Computer Engineering,2009,35(7):170-171,202. |
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| Authors: | CAI Zhao-quan HUANG Han |
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| Affiliation: | 1.Network Center;Huizhou University;Huizhou 516007;2.School of Software Engineering;South China University of Technology;Guangzhou 510006 |
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| Abstract: | Classical Particle Swarm Optimization(PSO) algorithm has bad diversity and is easy to converge locally.This paper puts forward a smallest-variation-first mutation to design an improved CLPSO algorithm named as CLPSO-M algorithm.The experimental result of solving the benchmark problems indicates that CLPSO-M performs better and more steadily than CLPSO. |
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