计算机科学 ›› 2013, Vol. 40 ›› Issue (Z11): 143-146.

• 智能控制与优化 • 上一篇    下一篇

基于杂交变异的动态粒子群优化算法

周利军,彭卫,曾小强,邹芳   

  1. 四川农业大学资源环境学院 成都611130;四川农业大学商学院 成都611830;四川农业大学商学院 成都611830;四川农业大学商学院 成都611830
  • 出版日期:2018-11-16 发布日期:2018-11-16
  • 基金资助:
    本文受四川省教育厅青年基金(11ZB058)资助

Dynamic Particle Swarm Optimization Based on Hybrid Variable

ZHOU Li-jun,PENG Wei,ZENG Xiao-qiang and ZOU Fang   

  • Online:2018-11-16 Published:2018-11-16

摘要: 粒子群优化算法(PSO)的结构相对简单、运行速度很快,但是算法极易陷入局部最优,出现早熟收敛现象。针对标准粒子群算法存在的问题,引入了一种随迭代次数和粒子间距离大小动态改变的惯性权重,通过设置比例系数控制二者对惯性权重的影响力度。在此基础上为了增加种群多样性,又引入“杂交变异”算子,设计了一种基于杂交变异的动态粒子群优化算法(HV-DPSO)。通过对基准函数的数值试验表明,新算法相对于标准粒子群算法不仅能有效地避免早熟收敛,而且具有更好的收敛效果。

关键词: 粒子群优化算法,动态惯性权重,杂交变异,早熟收敛,多样性

Abstract: Particle swarm optimization(PSO) is a relatively simple structure which runs very quickly,but it is easily fall into local optimum and appears the phenomenon of premature convergence.Aiming at the PSO existing problems,by setting the proportional coefficient control of inertia weight between influence strength,this paper introduced a kind of novel way using the iteration number and particle size of the distance between the dynamic change inertia weight.At the same time,in order to increase the diversity of population,using "hybrid variation" operator,designed a kind of dynamic particle swarm optimization based on hybrid variable,(HV-DPSO) based on reference function of numerical experiment.The experimental results show that compared with the traditional PSO,the new algorithm not only can effectively avoid premature convergence but also has better convergence effect.

Key words: Particle swarm optimization,Dynamic inertial weight,Hybrid variation,premature convergence,Diversity

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