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Multifactorial Particle Swarm Optimization Enhanced by Hybridization With Firefly Algorithm

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  • Heng Xiao

    (Osaka University, Japan)

  • Yokoya

    (Osaka University, Japan)

  • Toshiharu Hatanaka

    (The University of Fukuchiyama, Japan)

Abstract

In recent years, evolutionary multitasking has received attention in the evolutionary computation community. As an evolutionary multifactorial optimization method, multifactorial evolutionary algorithm (MFEA) is proposed to realize evolutionary multitasking. One concept called the skill factor is introduced to assign a preferred task for each individual in MFEA. Then, based on the skill factor, there are some multifactorial optimization solvers including swarm intelligence that have been developed. In this paper, a PSO-FA hybrid model with a model selection mechanism triggered by updating the personal best memory is applied to multifactorial optimization. The skill factor reassignment is introduced in this model to enhance the search capability of the hybrid swarm model. Then numerical experiments are carried out by using nine benchmark problems based on typical multitask situations and by comparing with a simple multifactorial PSO to show the effectiveness of the proposed method.

Suggested Citation

  • Heng Xiao & Yokoya & Toshiharu Hatanaka, 2021. "Multifactorial Particle Swarm Optimization Enhanced by Hybridization With Firefly Algorithm," International Journal of Swarm Intelligence Research (IJSIR), IGI Global, vol. 12(3), pages 172-187, July.
    • Handle: RePEc:igg:jsir00:v:12:y:2021:i:3:p:172-187
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