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A hybrid evolutionary multiobjective optimization algorithm with adaptive multi-fitness assignment

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Abstract

There are several studies on hybrid multi-operator recombination methods, while few works have been proposed in the area of combining different fitness assignment in a framework. On the other hand, it is known that fitness assignment has a marked impact on the performance of evolutionary multiobjective optimization algorithm (EMOA). In this paper, a hybrid EMOA is proposed, which divides the population into several smaller subpopulations according to their distribution in the objective space. Each subpopulation is evolved by an individual EMOA, and a hybrid performance measure estimates the performance of these EMOAs. We focus on the fitness assignment and assume that all EMOAs used in the subpopulations adopt the same recombination operator. To evaluate performance of the proposed algorithm, we compare it with MOEA/D-M2M, MOE-A/D, SMS-EMOA and NSGA-II on 16 test instances. Experimental results show that the proposed algorithm performs better than or similar to those compared EMOAs.

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Acknowledgments

The authors would like to thank Prof. Q. Zhang for giving many suggestions. This work was supported by the Natural Science Foundation of Guangdong Province (S2011030002886, S2012010008813) and the Programme of Science and Technology of Guangzhou (2014J4100209).

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Authors and Affiliations

  1. Guangdong University of Technology, Guangdong, China

    Fangqing Gu & Hai-Lin Liu

  2. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

    Kay Chen Tan

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  1. Fangqing Gu
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  2. Hai-Lin Liu
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  3. Kay Chen Tan
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Correspondence to Hai-Lin Liu.

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Communicated by V. Loia.

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Gu, F., Liu, HL. & Tan, K.C. A hybrid evolutionary multiobjective optimization algorithm with adaptive multi-fitness assignment. Soft Comput 19, 3249–3259 (2015). https://doi.org/10.1007/s00500-014-1480-9

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  • DOI: https://doi.org/10.1007/s00500-014-1480-9

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