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	Constrained multi-objective optimization evolutionary algorithm
	Wang Yuexuan ; Liu Lianchen ; Mu Shengling ; Wu Cheng
	2010-05-06 ; 2010-05-06
关键词	Theoretical or Mathematical/ constraint handling genetic algorithms Pareto optimisation probability/ genetic algorithm constrained multi-objective optimization problem infeasibility degree selection Pareto solution constraint handling/ B0260 Optimisation techniques B0240Z Other topics in statistics C1180 Optimisation techniques C1140Z Other topics in statistics
中文摘要	Genetic algorithms for constrained multi-objective optimization problems mainly focus on optimizing the conflicting multiple objectives without considering the constraint conditions. This paper describes a genetic algorithm which uses neighborhood comparisons and archiving in the genetic algorithm to smooth the conflicting objectives. Infeasibility degree selection is used to handle the constraints with the constraint domain principle applied to guide the evolutionary process. Two classic difficult problems constrained multi-objective optimization were analyzed by the algorithm to show that the method can find feasible Pareto solutions with a large probability.
语种	英语 ; 英语
出版者	Tsinghua Univ. Press ; China
内容类型	期刊论文
源URL	[http://hdl.handle.net/123456789/8929]
专题	清华大学
推荐引用方式 GB/T 7714	Wang Yuexuan,Liu Lianchen,Mu Shengling,et al. Constrained multi-objective optimization evolutionary algorithm[J],2010, 2010.
APA	Wang Yuexuan,Liu Lianchen,Mu Shengling,&Wu Cheng.(2010).Constrained multi-objective optimization evolutionary algorithm..
MLA	Wang Yuexuan,et al."Constrained multi-objective optimization evolutionary algorithm".(2010).

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