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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
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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     
Received: 05 August 2009      Published: 06 July 2010
CLC:  TP18  
Fund:  Project  (No.  0521010020)  supported  by  the  A*Star  (Agency  for Science, Technology and Research), Singapore
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Bo-yang Qu
Ponnuthurai-Nagaratnam Suganthan
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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/10.1631/jzus.C0910481     OR     http://www.zjujournals.com/xueshu/fitee/Y2010/V11/I7/538


Multi-objective differential evolution with 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.

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