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A cooperative approach for combining particle swarm optimization and differential evolution algorithms to solve single-objective optimization problems

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Abstract

The present paper proposes a new algorithm designed for solving optimization problems. This algorithm is a hybrid of Differential Evolution (DE) and Particle Swarm Optimization (PSO) algorithms. The proposed algorithm uses a coalition or cooperation model in the game theory to combine the DE and PSO algorithms. This is done in an attempt to keep a balance between the exploration and exploitation capabilities by preventing population stagnation and avoiding the local optimum. The DE and PSO algorithms are two players in the state space, which play cooperative games together using the Nash bargaining theory to find the best solution. To evaluate the performance of the proposed algorithm, 25 benchmark functions are used in terms of the CEC2005 structure. The proposed algorithm is then compared with the classical DE and PSO algorithms and the hybrid algorithms recently proposed. The results indicated that the proposed hybrid algorithm outperformed the classical algorithms and other hybrid models.

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Marziyeh Dadvar & Mitra Mirzarezaee

  2. Department of Mathematics and Computer Sciences, Shahed University, Tehran, Iran

    Hamidreza Navidi & Hamid Haj Seyyed Javadi

Authors
  1. Marziyeh Dadvar
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  2. Hamidreza Navidi
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  3. Hamid Haj Seyyed Javadi
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  4. Mitra Mirzarezaee
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The first author wrote the whole manuscript. The second author (corresponding author) helped with the algorithm. The last two authors are the supervisor and advisor of this study.

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Correspondence to Hamidreza Navidi.

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Dadvar, M., Navidi, H., Javadi, H.H.S. et al. A cooperative approach for combining particle swarm optimization and differential evolution algorithms to solve single-objective optimization problems. Appl Intell 52, 4089–4108 (2022). https://doi.org/10.1007/s10489-021-02605-x

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  • DOI: https://doi.org/10.1007/s10489-021-02605-x

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