计算机科学 ›› 2014, Vol. 41 ›› Issue (3): 59-65.

• 2013' 粗糙集 • 上一篇    下一篇

一种权重递增的粒子群算法

刘建华,张永晖,周理,贺文武   

  1. 福建工程学院信息科学与工程学院 福州350108;福建工程学院信息科学与工程学院 福州350108;福建工程学院信息科学与工程学院 福州350108;福建工程学院数理系 福州350108
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受福建省科技厅重点项目(2012H0002),福建省自然科学基金(2012J01246,2J01247),福建工程学院启动基金(E0600100)资助

Particle Swarm Optimization with Weight Increasing

LIU Jian-hua,ZHANG Yong-hui,ZHOU Li and HE Wen-wu   

  • Online:2018-11-14 Published:2018-11-14

摘要: 粒子群算法(Particle Swarm Optimization,PSO)是仿真生物群体的社会行为的一种智能优化算法,现在已广泛应用到各种优化计算中。PSO算法的权重参数采用随迭代而递减的时变策略,权重时变值一般是根据试验结果来确定的,很少通过理论分析来选择权重。利用PSO算法的理论模型,分析权重值对算法的影响,并说明PSO算法采用时变权重的合理性。进一步根据分析模型,提出一种权重可以随迭代而递增的PSO算法模型。通过利用经典的基准函数,经仿真试验验证,这种权重递增的PSO算法优于传统权重递减的PSO算法,并且其性能与标准PSO算法相当。

关键词: 粒子群算法,权重递增,群体智能,进化计算 中图法分类号TP301.6文献标识码A

Abstract: Particle swarm optimization (PSO) is an intelligent algorithm which simulates the social behavior of bird swarm or fish group and has been applied widely in all kinds of fields on optimization computation.The inertia weight of PSO has employed the policy of decreasing progressively with iteration,but the variable value of inertia weight is decided in term of the experiment and rarely analyzed with theory.This paper analyzed the inertia weight of PSO with the theoretical modal.And then a kind of PSO modal with inertia weight increasing progressively with iteration was provided.The benchmark functions were used to conduct the experiment.The test results show that PSO with weight increasing is superior to the traditional PSO with weight decreasing and can match with standard PSO.

Key words: Particle swarm optimization,Progressive weight increase,Swarm intelligence,Evolutionary computation

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