Robust optimization using multi-objective particle swarm optimization

被引：5
|
作者：
Ono S. [1 ]
Yoshitake Y. [1 ]
Nakayama S. [1 ]
机构：
[1] Department of Information Science and Biomedical Engineering, Graduate School of Science and Engineering, Kagoshima University, Kagoshima 890-0065
来源：
Artificial Life and Robotics | 2009年 / 14卷 / 02期
关键词：
Genetic algorithm; Multi-objective optimization; Particle swarm optimization; Robust solution search; Swarm intelligence;
D O I：
10.1007/s10015-009-0647-4
中图分类号：
学科分类号：
摘要：
This article proposes an algorithm to search for solutions which are robust against small perturbations in design variables. The proposed algorithm formulates robust optimization as a bi-objective optimization problem, and fi nds solutions by multi-objective particle swarm optimization (MOPSO). Experimental results have shown that MOPSO has a better performance at fi nding multiple robust solutions than a previous method using a multi-objective genetic algorithm. © 2009 International Symposium on Artificial Life and Robotics (ISAROB).
引用
收藏
页码：174 / 177
页数：3
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