用多目标进化算法搜索MOPs的鲁棒Pareto最优解

郑金华;罗 彪;周 聪;李望移

电子学报 ›› 2009, Vol. 37 ›› Issue (12) : 2815-2822.

PDF(552 KB)
PDF(552 KB)
电子学报 ›› 2009, Vol. 37 ›› Issue (12) : 2815-2822.
论文

用多目标进化算法搜索MOPs的鲁棒Pareto最优解

  • 郑金华, 罗 彪, 周 聪, 李望移
作者信息 +

Searching for Robust Pareto Optimal Solutions for MOPs with MOEA

  • ZHENG Jin-hua, LUO Biao, ZHOU Cong, LI Wang-yi
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文章历史 +

摘要

搜索鲁棒Pareto最优解是多目标进化算法(MOEA)研究的一个重要方面.目前,优化"原目标函数"的传统MOEA与基于"有效目标函数"的MOEA (Eff-MOEA)在搜索鲁棒Pareto最优解时都易丢失某些性质的解.为解决这一缺陷,本文定义了一种新的鲁棒Pareto最优解,提出了一种新的搜索鲁棒Pareto最优解的MOEA(MOEA/R),MOEA/R将多目标鲁棒优化问题(MROP)转化成两目标问题来优化,一个目标为解的质量,另一个目标为解的鲁棒性,每一目标均对应一子优化问题.通过与NSGA-Ⅱ及Eff-MOEA的对比分析,结果表明MOEA/R的结果较好,更重要的是本文探索了一种新的搜索鲁棒Pareto最优解的思想.

Abstract

Searching for robust Pareto optimal solutions is one of the most important fields in the research of multi-objective evolutionary algorithm (MOEA).Recently,both traditional MOEA and EFF-MOEA which optimize "original objective function" and "effective objective function" respectively easily lose some kinds of solutions.In order to solve this deficiency,we defined a new robust Pareto optimal solution and proposed a novel MOEA named as MOEA/R,which converts a multi-objective robust optimization problem (MROP) into a bi-objective optimization problem.Each of the two objectives represents a sub-MOP,one optimizes solution’s quality and the other optimizes solution’s robustness.Through the comparison and analysis between MOEA/R,NSGA-Ⅱ and Eff-MOEA,the experimental results demonstrate that MOEA/R can acquire good purposes.The most important contribution of this paper is that MOEA/R explores a novel methodology for searching robust Pareto optimal solutions.

关键词

多目标进化算法 / 鲁棒性 / 质量 / 鲁棒Pareto最优解 / 有效目标函数

Key words

MOEA / robustness / quality / robust Pareto optimal solutions / effective objective function

引用本文

导出引用
郑金华;罗 彪;周 聪;李望移. 用多目标进化算法搜索MOPs的鲁棒Pareto最优解[J]. 电子学报, 2009, 37(12): 2815-2822.
ZHENG Jin-hua;LUO Biao;ZHOU Cong;LI Wang-yi. Searching for Robust Pareto Optimal Solutions for MOPs with MOEA[J]. Acta Electronica Sinica, 2009, 37(12): 2815-2822.
中图分类号: TP18   

基金

国家自然科学基金 (No.60773047); 湖南省自然科学基金 (No.09JJ6089); 湖南省教育厅重点科研项目 (No.06A074)
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国家自然科学基金(No.60773047);湖南省自然科学基金(No.09JJ6089);湖南省教育厅重点科研项目(No.06A074)
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