A New Evolutionary Algorithm for Solving Multi-Objective Optimization Problems  

作　　者：D Chen Wen-ping, Kang Li-shanState Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, Hubei, China 

出　　处：《Wuhan University Journal of Natural Sciences》2003年第S1期202-206,共5页武汉大学学报（自然科学英文版）

基　　金：Supported by the National Natural Science Foundation of China (6013301,60073043,70071042)

摘　　要：Multi-objective optimization is a new focus of evolutionary computation research. This paper puts forward a new algorithm, which can not only converge quickly, but also keep diversity among population efficiently, in order to find the Pareto-optimal set. This new algorithm replaces the worst individual with a newly-created one by 'multi-parent crossover' , so that the population could converge near the true Pareto-optimal solutions in the end. At the same time, this new algorithm adopts niching and fitness-sharing techniques to keep the population in a good distribution. Numerical experiments show that the algorithm is rather effective in solving some Benchmarks. No matter whether the Pareto front of problems is convex or non-convex, continuous or discontinuous, and the problems are with constraints or not, the program turns out to do well.Multi-objective optimization is a new focus of evolutionary computation research. This paper puts forward a new algorithm, which can not only converge quickly, but also keep diversity among population efficiently, in order to find the Pareto-optimal set. This new algorithm replaces the worst individual with a newly-created one by 'multi-parent crossover' , so that the population could converge near the true Pareto-optimal solutions in the end. At the same time, this new algorithm adopts niching and fitness-sharing techniques to keep the population in a good distribution. Numerical experiments show that the algorithm is rather effective in solving some Benchmarks. No matter whether the Pareto front of problems is convex or non-convex, continuous or discontinuous, and the problems are with constraints or not, the program turns out to do well.

关 键 词：evolutionary computation multi-objective optimization Pareto-optimal set fitness-sharing 

分 类 号：O224[理学—运筹学与控制论]