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Journal of Zhejiang University SCIENCE C 2010 Vol.11 No.7 P.538-543

10.1631/jzus.C0910481


Multi-objective differential evolution with diversity enhancement


Author(s):  Bo-yang Qu, Ponnuthurai-Nagaratnam Suganthan

Affiliation(s):  School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore

Corresponding email(s):   E070088@ntu.edu.sg, epnsugan@ntu.edu.sg

Key Words:  Multi-objective evolutionary algorithm (MOEA), Multi-objective differential evolution (MODE), Diversity enhancement


Bo-yang Qu, Ponnuthurai-Nagaratnam Suganthan. Multi-objective differential evolution with diversity enhancement[J]. Journal of Zhejiang University Science C, 2010, 11(7): 538-543.

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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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

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