An evolutionary algorithm for constrained multi-objective optimization problems

被引：0
|
作者：
Min, Hua-Qing [1 ,2 ]
Zhou, Yu-Ren [2 ]
Lu, Yan-Sheng [1 ]
Jiang, Jia-zhi [2 ]
机构：
[1] Huazhong Univ Sci & Technol, Coll Comp Sci & Engn, Wuhan 430074, Peoples R China
[2] South China Univ Technol, Sch Engn & Comp Sci, Guangzhou 510640, Peoples R China
来源：
APSCC: 2006 IEEE ASIA-PACIFIC CONFERENCE ON SERVICES COMPUTING, PROCEEDINGS | 2006年
关键词：
evolutionary algorithms; multi-objective optimization; constrained;
D O I：
暂无
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Constrained multi-objective optimization problems (CMOP) are challenging and difficult to solve. In this paper, a simple and practical evolutionary algorithm for constrained multi-objective optimization problems (EA CMOP) is presented, by defining constraints using non-parameter punitive functions, using Pareto strength value to represent Pareto order strength among individuals and using crowding density to ensure group diversity. It defines the evolutionary algorithm fitness functions by combining constraint treatment, comparison of Pareto strength optimization and crowding density. Test results on several benchmark functions showed that the approach is effective and robust.
引用
收藏
页码：667 / +
页数：2
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