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

页码：174 / 177

页数：3

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