计算机工程 ›› 2009, Vol. 35 ›› Issue (7): 170-171,.doi: 10.3969/j.issn.1000-3428.2009.07.059

• 人工智能及识别技术 • 上一篇    下一篇

自适应变异综合学习粒子群优化算法

蔡昭权1,黄 翰2   

  1. (1. 惠州学院网络中心,惠州 516007;2. 华南理工大学软件学院,广州 510006)
  • 收稿日期:1900-01-01 修回日期:1900-01-01 出版日期:2009-04-05 发布日期:2009-04-05

Comprehensive Learning Particle Swarm Optimization Algorithm with Adaptive Mutation

CAI Zhao-quan1, HUANG Han2   

  1. (1. Network Center, Huizhou University, Huizhou 516007; 2. School of Software Engineering, South China University of Technology, Guangzhou 510006)
  • Received:1900-01-01 Revised:1900-01-01 Online:2009-04-05 Published:2009-04-05

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。

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

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.

Key words: swarm intelligence, Particle Swarm Optimization(PSO) alogorithm, comprehensive learning, smallest variation first, adaptive mutation

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