Multi-objective differential evolution with diversity enhancement

作     者：Ponnuthurai-Nagaratnam SUGANTHAN 

作者机构：School of Electrical and Electronic EngineeringNanyang Technological University 

出 版 物：《Journal of Zhejiang University-Science C(Computers and Electronics)》 (浙江大学学报C辑（计算机与电子（英文版）)

年 卷 期：2010年第11卷第7期

页      面：538-543页

核心收录：

学科分类：0810[工学-信息与通信工程] 12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 07[理学] 0805[工学-材料科学与工程（可授工学、理学学位）] 070105[理学-运筹学与控制论] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Project(No.0521010020)supported by the A*Star(Agency for Science Technology and Research) Singapore 

主　　题：Multi-objective evolutionary algorithm (MOEA) Multi-objective differential evolution (MODE) Diversity enhancement 

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://***/home/epnsugan/. With the proposed method, the performances were either better than or equal to those of the MODE without the diversity enhancement.