Particle swarm optimization with preference order ranking for multi-objective optimization

被引：144
|
作者：
Wang, Yujia [1 ]
Yang, Yupu [1 ]
机构：
[1] Shanghai Jiao Tong Univ, Dept Automat, Shanghai 200240, Peoples R China
来源：
INFORMATION SCIENCES | 2009年 / 179卷 / 12期
关键词：
Particle swarm; Preference order; Pareto dominance; Multi-objective optimization; Best compromise; GENETIC ALGORITHM; OBJECTIVES; TIME;
D O I：
10.1016/j.ins.2009.01.005
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A new optimality criterion based on preference order (PO) scheme is used to identify the best compromise in multi-objective particle swarm optimization (MOPSO). This scheme is more efficient than Pareto ranking scheme, especially when the number of objectives is very large. Meanwhile, a novel updating formula for the particle's velocity is introduced to improve the search ability of the algorithm. The proposed algorithm has been compared with NSGA-II and other two MOPSO algorithms. The experimental results indicate that the proposed approach is effective on the highly complex multi-objective optimization problems. (c) 2009 Elsevier Inc. All rights reserved.
引用
收藏
页码：1944 / 1959
页数：16
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