Multi-objective particle swarm optimization with two normal mutations

被引：0
|
作者：
Gao, Sheng-Guo [1 ]
Wu, Zhong [1 ]
Li, Xu-Fang [2 ,3 ]
Liu, Sheng [1 ]
机构：
[1] School of Management, Shanghai University of Engineering Science, Shanghai,201620, China
[2] School of Economics & Management, Tongji University, Shanghai,200092, China
[3] Shanghai Key Laboratory of Data Science, Fudan University, Shanghai,200433, China
来源：
Kongzhi yu Juece/Control and Decision | 2015年 / 30卷 / 05期
关键词：
Particle swarm optimization (PSO) - Pareto principle - Optimal systems;
D O I：
10.13195/j.kzyjc.2014.0426
中图分类号：
学科分类号：
摘要：
A particle swarm algorithm with two types of normal mutations is proposed for the multi-objective problem. One of variations contributes to discover new Pareto optimal solutions in the neighborhoods of these existing solutions, the other can disperse the swarm. The searching process is divided into three stages, and those particles which guide the others are selected with different targeted strategies in each stage. Numerical results show that the algorithm can significantly improve the diversity and convergence of the Pareto optimal solution. ©, 2015, Northeast University. All right reserved.
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页码：939 / 942
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