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A hybrid genetic-particle swarm optimizer using precise mutation strategy for computationally expensive problems

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Abstract

Rational use of resources and efficient production are the focus of today’s society, which makes it urgent to develop an algorithm that can solve most problems and has intelligent characteristics. Particle swarm optimization (PSO) and genetic algorithm (GA) are evolutionary algorithms that have been applied successfully in various complex fields. However, both have their advantages and disadvantages. A hybrid GA-PSO algorithm that employs a precise mutation strategy (PMGPSO) is developed in this paper to utilize their outstanding abilities fully and minimize their shortcomings. In PMGPSO, the precise mutation strategy related to two clustering coefficients is proposed. This strategy distinguishes the degree of required mutation by particle swarm in different periods and coordinates the performance of exploration and exploitation. Furthermore, adopt a sort-based selection method to accelerate the convergence speed and cross the poor particles with the excellent particles to expand the search range. Chaotic sequences are used to generate initial particles and update inertia weight. The performance of PMGPSO is studied with 12 basic benchmark functions and 20 functions extracted from the CEC’2014 test suite. The comparison results with some representative algorithms and existing data demonstrate that PMGPSO provides a high-accuracy solution with a faster convergence speed.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant No.12072160, 11972194).

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  1. School of Energy and Power Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China

    Xiongcheng Duan & Xiaobing Zhang

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Correspondence to Xiaobing Zhang.

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Duan, X., Zhang, X. A hybrid genetic-particle swarm optimizer using precise mutation strategy for computationally expensive problems. Appl Intell 52, 8510–8533 (2022). https://doi.org/10.1007/s10489-021-02828-y

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