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Multi-objective particle swarm-differential evolution algorithm

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Abstract

A multi-objective particle swarm-differential evolution algorithm (MOPSDE) is proposed that combined a particle swarm optimization (PSO) with a differential evolution (DE). During consecutive generations, a scale factor is produced by using a proposed mechanism based on the simulated annealing method and is applied to dynamically adjust the percentage of use of PSO and DE. In addition, the mutation operation of DE is improved, to satisfy that the proposed algorithm has different mutation operation in different searching stage. As a result, the capability of the local searching is enhanced and the prematurity of the population is restrained. The effectiveness of the proposed method has been validated through comprehensive tests using benchmark test functions. The numerical results obtained by this algorithm are compared with those obtained by the improved non-dominated sorting genetic algorithm (NSGA-II) and the other algorithms mentioned in the literature. The results show the effectiveness of the proposed MOPSDE algorithm.

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Acknowledgments

The work is supported by the Natural Science Foundation of Hubei Province, China (#2015cfb586).

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  1. School of Automation, Wuhan University of Technology, Wuhan, 430070, China

    Yi-xin Su & Rui Chi

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  1. Yi-xin Su
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  2. Rui Chi
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Correspondence to Yi-xin Su or Rui Chi.

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Su, Yx., Chi, R. Multi-objective particle swarm-differential evolution algorithm. Neural Comput & Applic 28, 407–418 (2017). https://doi.org/10.1007/s00521-015-2073-y

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  • DOI: https://doi.org/10.1007/s00521-015-2073-y

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