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

中图分类号: 

• TP301.6