自适应变异综合学习粒子群优化算法

Comprehensive Learning Particle Swarm Optimization Algorithm with Adaptive Mutation

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。

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.