西南交通大学学报2012，Vol.47Issue(5)：761-768,8.DOI:10.3969/j.issn.0258-2724.2012.05.006
基于个体最优位置的自适应变异扰动粒子群算法
Adaptive Mutation Disturbance Particle Swarm Optimization Algorithm Based on Personal Best Position
摘要
Abstract
In order to overcome the disadvantage of the particle swarm optimization (PSO) that it easily falls into local optimum, an adaptive mutation disturbance particle swarm optimization (AMDPSO) algorithm based on personal best position was proposed. This algorithm is based on PSO, and the disturbance is considered. When the adaptive conditions are met, the mutation operation of particles is performed based on the personal best position. The proposed algorithm was applied to 6 test functions and compared with IWPSO ( inertia weight particle swarm optimization ) , CFPSO (constriction factor particle swarm optimization) and DE (differential evolution). The research results show that the AMDPSO has a good convergence rate and optimization capability, and can easily escape the local optimum and keep the population diversity.关键词
粒子群算法/个体最优位置/自适应变异/扰动Key words
particle swarm optimization/ personal best position/ adaptive mutation/ disturbance分类
信息技术与安全科学引用本文复制引用
刘志刚,曾嘉俊,韩志伟..基于个体最优位置的自适应变异扰动粒子群算法[J].西南交通大学学报,2012,47(5):761-768,8.基金项目
国家自然科学基金资助项目(U1134205,51007074) （U1134205,51007074）
教育部新世纪优秀人才支持计划资助项目(NECT-08-0825) （NECT-08-0825）
中央高校基本科研业务费专项资金资助项目(SWJTU11CX141) （SWJTU11CX141）
