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GEPSO: A new generalized particle swarm optimization algorithm

Author

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  • Sedighizadeh, Davoud
  • Masehian, Ellips
  • Sedighizadeh, Mostafa
  • Akbaripour, Hossein

Abstract

Particle Swarm Optimization (PSO) algorithm is a nature-inspired meta-heuristic that has been utilized as a powerful optimization tool in a wide range of applications since its inception in 1995. Due to the flexibility of its parameters and concepts, PSO has appeared in many variants, probably more than any other meta-heuristic algorithm. This paper introduces the Generalized Particle Swarm Optimization (GEPSO) algorithm as a new version of the PSO algorithm for continuous space optimization, which enriches the original PSO by incorporating two new terms into the velocity updating equation. These terms aim to deepen the interrelations of particles and their knowledge sharing, increase variety in the swarm, and provide a better search in unexplored areas of the search space. Moreover, a novel procedure is utilized for dynamic updating of the particles’ inertia weights, which controls the convergence of the swarm towards a solution. Also, since parameters of heuristic and meta-heuristic algorithms have a significant influence on their performance, a comprehensive guideline for parameter tuning of the GEPSO is developed. The computational results of solving numerous well-known benchmark functions by the GEPSO, original PSO, Repulsive PSO (REPSO), PSO with Passive Congregation (PSOPC), Negative PSO (NPSO), Deterministic PSO (DPSO), and Line Search-Based Derivative-Free PSO (LS-DF-PSO) approaches showed that the GEPSO outperformed the compared methods in terms of mean and standard deviation of fitness function values and runtimes.

Suggested Citation

  • Sedighizadeh, Davoud & Masehian, Ellips & Sedighizadeh, Mostafa & Akbaripour, Hossein, 2021. "GEPSO: A new generalized particle swarm optimization algorithm," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 179(C), pages 194-212.
    • Handle: RePEc:eee:matcom:v:179:y:2021:i:c:p:194-212
    DOI: 10.1016/j.matcom.2020.08.013
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    References listed on IDEAS

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    1. Luan, Jing & Yao, Zhong & Zhao, Futao & Song, Xin, 2019. "A novel method to solve supplier selection problem: Hybrid algorithm of genetic algorithm and ant colony optimization," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 156(C), pages 294-309.
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    2. González-Parra, Gilberto & Villanueva-Oller, Javier & Navarro-González, F.J. & Ceberio, Josu & Luebben, Giulia, 2024. "A network-based model to assess vaccination strategies for the COVID-19 pandemic by using Bayesian optimization," Chaos, Solitons & Fractals, Elsevier, vol. 181(C).
    3. Kezong Tang & Xiong-Fei Wei & Yuan-Hao Jiang & Zi-Wei Chen & Lihua Yang, 2023. "An Adaptive Ant Colony Optimization for Solving Large-Scale Traveling Salesman Problem," Mathematics, MDPI, vol. 11(21), pages 1-26, October.
    4. Martín Montes Rivera & Carlos Guerrero-Mendez & Daniela Lopez-Betancur & Tonatiuh Saucedo-Anaya, 2023. "Dynamical Sphere Regrouping Particle Swarm Optimization: A Proposed Algorithm for Dealing with PSO Premature Convergence in Large-Scale Global Optimization," Mathematics, MDPI, vol. 11(20), pages 1-40, October.
    5. Yang, Xu & Li, Hongru, 2023. "Multi-sample learning particle swarm optimization with adaptive crossover operation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 208(C), pages 246-282.
    6. Abd-Elhaleem, Sameh & Shoeib, Walaa & Sobaih, Abdel Azim, 2023. "A new power management strategy for plug-in hybrid electric vehicles based on an intelligent controller integrated with CIGPSO algorithm," Energy, Elsevier, vol. 265(C).

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