Constraint Handling in Multiobjective Evolutionary Optimization

被引:304
作者
Woldesenbet, Yonas Gebre [1 ]
Yen, Gary G. [1 ]
Tessema, Biruk G. [1 ]
机构
[1] Oklahoma State Univ, Sch Elect & Comp Engn, Stillwater, OK 74078 USA
关键词
Constraint handling; evolutionary multiobjective optimization; genetic algorithm; GENETIC ALGORITHM; RANK;
D O I
10.1109/TEVC.2008.2009032
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper proposes a constraint handling technique for multiobjective evolutionary algorithms based on an adaptive penalty function and a distance measure. These two functions vary dependent upon the objective function value and the sum of constraint violations of an individual. Through this design, the objective space is modified to account for the performance and constraint violation of each individual. The modified objective functions are used in the nondominance sorting to facilitate the search of optimal solutions not only in the feasible space but also in the infeasible regions. The search in the infeasible space is designed to exploit those individuals with better objective values and lower constraint violations. The number of feasible individuals in the population is used to guide the search process either toward finding more feasible solutions or favor in search for optimal solutions. The proposed method is simple to implement and does not need any parameter tuning. The constraint handling technique is tested on several constrained multiobjective optimization problems and has shown superior results compared to some chosen state-of-the-art designs.
引用
收藏
页码:514 / 525
页数:12
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