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Front. Inform. Technol. Electron. Eng.  2010, Vol. 11 Issue (7): 538-543    DOI: 10.1631/jzus.C0910481
    
Multi-objective differential evolution with diversity enhancement
Bo-yang Qu, Ponnuthurai-Nagaratnam Suganthan*
School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore
Multi-objective differential evolution with diversity enhancement
Bo-yang Qu, Ponnuthurai-Nagaratnam Suganthan*
School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore
 全文: PDF 
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.
关键词： Multi-objective evolutionary algorithm (MOEA);  Multi-objective differential evolution (MODE);  Diversity enhancement    
Abstract: 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.
Key words: Multi-objective evolutionary algorithm (MOEA)    Multi-objective differential evolution (MODE)    Diversity enhancement
收稿日期: 2009-08-05 出版日期: 2010-07-06
CLC:  TP18  
基金资助: Project  (No.  0521010020)  supported  by  the  A*Star  (Agency  for Science, Technology and Research), Singapore
通讯作者: Ponnuthurai-Nagaratnam SUGANTHAN     E-mail: E070088@ntu.edu.sg, epnsugan@ntu.edu.sg
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Bo-yang Qu, Ponnuthurai-Nagaratnam Suganthan. Multi-objective differential evolution with diversity enhancement. Front. Inform. Technol. Electron. Eng., 2010, 11(7): 538-543.

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http://www.zjujournals.com/xueshu/fitee/CN/10.1631/jzus.C0910481    或    http://www.zjujournals.com/xueshu/fitee/CN/Y2010/V11/I7/538

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