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基于个体最优位置的自适应变异扰动粒子群算法

刘志刚,  曾嘉俊,  韩志伟

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文章导航 > 西南交通大学学报  > 2012 >  25(5): 761-768.
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, 25(5): 761-768. doi: 10.3969/j.issn.0258-2724.2012.05.006
引用本文: 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, 25(5): 761-768. doi: 10.3969/j.issn.0258-2724.2012.05.006
shu
LIU Zhigang, ZENG Jiajun, HAN Zhiwei. Adaptive Mutation Disturbance Particle Swarm Optimization Algorithm Based on Personal Best Position[J]. Journal of Southwest Jiaotong University, 2012, 25(5): 761-768. doi: 10.3969/j.issn.0258-2724.2012.05.006
Citation: LIU Zhigang, ZENG Jiajun, HAN Zhiwei. Adaptive Mutation Disturbance Particle Swarm Optimization Algorithm Based on Personal Best Position[J]. Journal of Southwest Jiaotong University, 2012, 25(5): 761-768. doi: 10.3969/j.issn.0258-2724.2012.05.006
shu

基于个体最优位置的自适应变异扰动粒子群算法

doi: 10.3969/j.issn.0258-2724.2012.05.006
基金项目: 

国家自然科学基金资助项目(U1134205, 51007074)

教育部新世纪优秀人才支持计划资助项目(NECT-08-0825)

中央高校基本科研业务费专项资金资助项目(SWJTU11CX141)

Adaptive Mutation Disturbance Particle Swarm Optimization Algorithm Based on Personal Best Position

    摘要
  • 摘要: 针对粒子群算法在寻优时容易陷入局部最优的不足,提出了一种基于个体最优位置的自适应变异扰动粒子群算法AMDPSO (adaptive mutation disturbance particle swarm optimization).该算法以粒子群算法为基础,加入扰动,当满足自适应条件时,粒子以个体最优位置为依据进行变异操作.将该算法运用于6个测试函数,并与惯性权重粒子群算法、收缩因子粒子群算法以及差分进化算法进行了比较,结果表明:AMDPSO能在寻优过程中让粒子跳出局部最优,保持种群多样性,具有更好的收敛速度和优化性能.

     

    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.

     

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  • 收稿日期:  2012-05-08
  • 刊出日期:  2012-10-25

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