Multi-objective differential evolution with diversity enhancement 被引量：2

Multi-objective differential evolution with diversity enhancement

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摘要 Multi-objective differential evolution (MODE) is a powerful and efficient population-based stochastic search technique for solving multi-objective optimization problems in many scientific and engineering fields. However, premature convergence is the major drawback of MODE, especially when there are numerous local Pareto optimal solutions. To overcome this problem, we propose a MODE with a diversity enhancement (MODE-DE) mechanism to prevent the algorithm becoming trapped in a locally optimal Pareto front. The proposed algorithm combines the current population with a number of randomly generated parameter vectors to increase the diversity of the differential vectors and thereby the diversity of the newly generated offspring. The performance of the MODE-DE algorithm was evaluated on a set of 19 benchmark problem codes available from http://www3.ntu.edu.sg/home/epnsugan/. With the proposed method, the performances were either better than or equal to those of the MODE without the diversity enhancement. Multi-objective differential evolution （MODE） is a powerful and efficient population-based stochastic search technique for solving multi-objective optimization problems in many scientific and engineering fields. However, premature convergence is the major drawback of MODE, especially when there are numerous local Pareto optimal solutions. To overcome this problem, we propose a MODE with a diversity enhancement （MODE-DE） mechanism to prevent the algorithm becoming trapped in a locally optimal Pareto front. The proposed algorithm combines the current population with a number of randomly generated parameter vectors to increase the diversity of the differential vectors and thereby the diversity of the newly generated offspring. The performance of the MODE-DE algorithm was evaluated on a set of 19 benchmark problem codes available from http：//www3.ntu.edu.sg/home/epnsugan/. With the proposed method, the performances were either better than or equal to those of the MODE without the diversity enhancement.

作者 Ponnuthurai-Nagaratnam SUGANTHAN

机构地区 School of Electrical and Electronic Engineering

出处《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2010年第7期538-543,共6页 浙江大学学报C辑（计算机与电子（英文版）

基金 Project(No.0521010020)supported by the A*Star(Agency for Science,Technology and Research),Singapore

关键词 Multi-objective evolutionary algorithm (MOEA) Multi-objective differential evolution (MODE) Diversity enhancement Multi-objective evolutionary algorithm （MOEA）, Multi-objective differential evolution （MODE）, Diversityenhancement

分类号 O221.6 [理学—运筹学与控制论]

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