吉林大学学报(工学版) ›› 2017, Vol. 47 ›› Issue (2): 615-623.doi: 10.13229/j.cnki.jdxbgxb201702037

• 论文 • 上一篇    下一篇

保证全局收敛的随机粒子群新算法

孙亮, 徐海浪, 葛宏伟   

  1. 大连理工大学 计算机科学与技术学院,辽宁 大连 116023
  • 收稿日期:2015-12-03 出版日期:2017-03-20 发布日期:2017-03-20
  • 通讯作者: 葛宏伟(1978-),男,副教授,博士.研究方向:群智计算,机器学习,深度学习,智能交通及车联网技术.E-mail:hwge@dlut.edu.cn
  • 作者简介:孙亮(1981-),男,在站博士后.研究方向:机器学习,群智计算,智能优化与仿真.E-mail:liangsun@dlut.edu.cn
  • 基金资助:
    国家自然科学基金项目(61572104, 61103146, 61402076); 辽宁省科技厅博士启动项目(20141023); 中央高校基本科研业务费专项项目(DUT15QY26, DUT14QY06); 吉林大学符号计算与知识工程教育部重点实验室项目(93K172016K11).

Novel global convergence stochastic particle swarm optimizers

SUN Liang, XU Hai-lang, GE Hong-wei   

  1. College of Computer Science and Technology, Dalian University of Technology, Dalian 116023, China
  • Received:2015-12-03 Online:2017-03-20 Published:2017-03-20

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摘要： 针对粒子群算法未成熟收敛的弱点进行了改进。给出了保证算法全局收敛的充分条件,即全局性假设条件和单调性假设条件,进而依据提出的全局收敛的充分条件,设计了具有柯西随机和高斯随机性质的粒子群算法。实验结果表明,本文提出的具有全局收敛性的粒子群算法能够有效地求解全局优化问题。

关键词: 计算机应用, 随机粒子群算法, 全局搜索, 局部搜索, 全局优化

Abstract: Particle Swarm Optimization (PSO) is a swarm intelligence based optimization algorithm. It is simple in concept, easy in implementation and fast in searching. This paper aims at improving the weak premature convergence shortcoming of traditional PSOs. Two sufficient conditions, i.e. global condition and local condition, which guarantee PSO converging to the optimality region, are proposed and verified. Moreover, two PSO variants that have Cauchy stochastic character and Gaussian stochastic character are designed based on the proposed conditions. The experimental results show that the proposed global convergent PSOs can solve the optimization problems effectively.

Key words: computer application, stochastic particle swarm optimization, global search, local search, global optimization

中图分类号: 

  • TP391
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