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
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
CLC Number:
CAI Zhao-quan; HUANG Han. Comprehensive Learning Particle Swarm Optimization Algorithm with Adaptive Mutation[J]. Computer Engineering, 2009, 35(7): 170-171,.
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,.