A New Multi-swarm Multi-objective Particle Swarm Optimization Based on Pareto Front Set

被引：0
|
作者：
Sun, Yanxia [1 ]
van Wyk, Barend Jacobus [1 ]
Wang, Zenghui [2 ]
机构：
[1] Tshwane Univ Technol, FSATI, ZA-0001 Pretoria, South Africa
[2] Univ South Africa, Sch Engn, ZA-0003 Pretoria, South Africa
来源：
ADVANCED INTELLIGENT COMPUTING THEORIES AND APPLICATIONS: WITH ASPECTS OF ARTIFICIAL INTELLIGENCE | 2012年 / 6839卷
关键词：
Multi-objective Optimization; Particle Swarm Optimization; Multiple swarms; Pareto front;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a new multi-swarm method is proposed for multi-objective particle swarm optimization. To enhance the Pareto front searching ability of PSO, the particles are divided into many swarms. Several swarms are dynamically searching the objective space around some points of the Pareto front set. The rest of particles are searching the space keeping away from the Pareto front to improve the global search ability. Simulation results and comparisons with existing Multi-objective Particle Swarm Optimization methods demonstrate that the proposed method effectively enhances the search efficiency and improves the search quality.
引用
收藏
页码：203 / +
页数：2
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