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Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure

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Abstract

A self-adaptive differential evolution algorithm incorporate Pareto dominance to solve multi-objective optimization problems is presented. The proposed approach adopts an external elitist archive to retain non-dominated solutions found during the evolutionary process. In order to preserve the diversity of Pareto optimality, a crowding entropy diversity measure tactic is proposed. The crowding entropy strategy is able to measure the crowding degree of the solutions more accurately. The experiments were performed using eighteen benchmark test functions. The experiment results show that, compared with three other multi-objective optimization evolutionary algorithms, the proposed MOSADE is able to find better spread of solutions with better convergence to the Pareto front and preserve the diversity of Pareto optimal solutions more efficiently.

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Acknowledgments

Authors would like to express their sincere thanks to the referees for their valuable suggestions and comments. This work was partly supported by the National Natural Science Foundation of P. R. China (Grant no. 60835004, 60775047), the National High Technology Research and Development Program of China (Grant no. 2007AA04Z244, 2008AA04Z214), Scientific Research Fund of Hunan Provincial Education Department (08C337), and the Program for New Century Excellent Talents in University.

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  1. College of Electrical and Information Engineering, Hunan University, 410082, Changsha, Hunan, China

    Yao-Nan Wang, Liang-Hong Wu & Xiao-Fang Yuan

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  1. Yao-Nan Wang
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  2. Liang-Hong Wu
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  3. Xiao-Fang Yuan
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Correspondence to Liang-Hong Wu.

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Wang, YN., Wu, LH. & Yuan, XF. Multi-objective self-adaptive differential evolution with elitist archive and crowding entropy-based diversity measure. Soft Comput 14, 193–209 (2010). https://doi.org/10.1007/s00500-008-0394-9

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