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

被引：190

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：

080201 [机械制造及其自动化];

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

页码：1944 / 1959

页数：16

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