计算机工程与应用 ›› 2008, Vol. 44 ›› Issue (15): 48-50.

• 理论研究 • 上一篇    下一篇

解多目标约束问题的改进MaxMin-PSO算法

黄圣杰,罗 琦,佟金颖   

  1. 南京信息工程大学 信息与控制学院,南京 210044
  • 收稿日期:2007-09-04 修回日期:2007-11-28 出版日期:2008-05-21 发布日期:2008-05-21
  • 通讯作者: 黄圣杰

Improved MaxMin Particle Swarm Optimization for solving multiobjective constrained optimization problems

HUANG Sheng-jie,LUO Qi,TONG Jin-ying   

  1. Department of Information and Communication,Nanjing University of Science and Technology,Nanjing 210044,China
  • Received:2007-09-04 Revised:2007-11-28 Online:2008-05-21 Published:2008-05-21
  • Contact: HUANG Sheng-jie

摘要: 将最大最小化适应度函数与罚函数相结合,提出了一种实用有效求解多目标约束优化问题的粒子群算法。采用归类和比较的思想进行替换非劣解;改变以往全局最优值的选取方法,而采用轮序方式从非劣解中获取。实验证明改进的MaxMin-PSO算法能更加有效的逼近Pareto解,收敛速度更快,分布更均匀,且能很好的抑制低维多目标约束问题的发散现象。

关键词: 粒子群算法, 最大最小适应函数, 轮序, 罚函数

Abstract: In this paper,Max-Min fitness function and penalty function are combined together,and a practical and effective particle swarm optimization algorithm is proposed to solve multi-objectives constrained optimization problems.Non-inferior solutions are replaced according to the idea of cluster and compare.The method of selecting globally optimal solution from non-inferior solutions in turn is adopted instead of the ancient method.The experimental results show that the modified MaxMin-PSO algorithm converges more quickly and efficiently to Pareto solutions and achieve a well distribution.It also restrains the radiation of low dimension multi-objectives constrained functions.

Key words: particle swarm algorithm, MaxMin function, turn list, punish function