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Hybridizing Particle Swarm Optimization with JADE for continuous optimization

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Abstract

As a population-based random search optimization technique, particle swarm optimization (PSO) has become an important branch of swarm intelligence (SI). To utilizing the advantage of operations in different SI, this study proposed a hybrid of multi-crossover operation and adaptive differential evolution with optional external archive (JADE), named PSOJADE, to balance the global and local search capabilities. In the experiments, the proposed algorithm is compared with six other advanced differential evolution (DE), PSO, and hybrid of DE and PSO techniques using 30 benchmark functions in CEC2017. To evaluate the effectiveness of the proposed PSOJADE more comprehensively, the experiments were implemented on 10-D, 30-D, and 50-D respectively. The experimental results indicate that the proposed algorithm yields better solution accuracy than the other techniques on 10-D, 30-D, and 50-D meanwhile.

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Acknowledgments

This work was supported by a grant from the Shandong Provincial Natural Science Foundation, China (Grant nos. ZR2017MF067 and 2016GGX101022).

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Authors and Affiliations

  1. School of Management Science and Engineering, Shandong University of Finance and Economics, Jinan, China

    Sheng-Yong Du

  2. School of Computer Science and Technology, Shandong University of Finance and Economics, Jinan, China

    Zhao-Guang Liu

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  1. Sheng-Yong Du
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  2. Zhao-Guang Liu
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Correspondence to Sheng-Yong Du.

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Du, SY., Liu, ZG. Hybridizing Particle Swarm Optimization with JADE for continuous optimization. Multimed Tools Appl 79, 4619–4636 (2020). https://doi.org/10.1007/s11042-019-08142-7

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  • DOI: https://doi.org/10.1007/s11042-019-08142-7

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