An Evolutionary Algorithm for Constrained Multi-objective Optimization Problems

Author

Min, Hua-Qing ; Zhou, Yu-Ren ; Lu, Yan-sheng ; Jiang, Jia-zhi

Author_Institution

Coll. of Comput. Sci. & Eng., HuaZhong Univ. of Sci. & Technol., Wuhan

fYear

2006

fDate

Dec. 2006

Firstpage

667

Lastpage

670

Abstract

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 (EACMOP) 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

Keywords

Pareto optimisation; evolutionary computation; Pareto order strength; Pareto strength optimization; Pareto strength value; constrained multiobjective optimization problem; constraint treatment; crowding density; evolutionary algorithm; group diversity; nonparameter punitive functions; Algorithm design and analysis; Benchmark testing; Computer science; Constraint optimization; Design optimization; Diversity reception; Educational institutions; Evolutionary computation; Pareto optimization; Robustness; Evolutionary algorithms; constrained; multi-objective optimization;

fLanguage

English

Publisher

ieee

Conference_Titel

Services Computing, 2006. APSCC '06. IEEE Asia-Pacific Conference on

Conference_Location

Guangzhou, Guangdong

Print_ISBN

0-7695-2751-5

Type

conf

DOI

10.1109/APSCC.2006.30

Filename

4041311