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A Survey of Modern Hybrid Particle Swarm Optimization Algorithms

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Applications of Evolutionary Computation (EvoApplications 2025)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15613))

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Abstract

Bio-inspired, population-based meta-heuristic for global optimization are very popular algorithms for addressing complex computational problems that traditional methods struggle to solve. Among the existing algorithms, the swarm intelligence algorithm Particle Swarm Optimization (PSO) is one of the most popular, thanks to its simplicity and effectiveness in multiple scenarios. This article focuses on recent hybrid optimization methods that extend the basic functioning of PSO. Hybridization, in this context, is defined as the integration of PSO with a different technique, to take advantage of the strengths of both algorithms. According to our findings, many variants have been proposed. The most frequent solutions consist of the hybridization of PSO with evolutionary operators (e.g. Genetic Algorithms and Differential Evolution); such strategies usually maintain a high degree of diversity into the population, enhancing global search capability, while reducing the risk of stagnation. Meanwhile the most widespread applications are from the areas of energy optimization, structural engineering and machine learning problems, demonstrating the versatility of these hybrid approaches.

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Acknowledgments

This work was supported by national funds through FCT (Fundação para a Ciência e a Tecnologia), under the project - UIDB/04152/2020 - Centro de Investigação em Gestão de Informação (MagIC)/NOVA IMS (https://doi.org/10.54499/UIDB/04152/2020).

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Authors and Affiliations

  1. Ca’ Foscari University, Venice, Italy

    Matteo Grazioso & Marco S. Nobile

  2. University of Turin, Turin, Italy

    Chiara Gallese

  3. NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisboa, Portugal

    Leonardo Vanneschi

Authors
  1. Matteo Grazioso
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  2. Chiara Gallese
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  3. Leonardo Vanneschi
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  4. Marco S. Nobile
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Correspondence to Matteo Grazioso .

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  1. University of Granada, Granada, Spain

    Pablo García-Sánchez

  2. Edinburgh Napier University, Edinburgh, UK

    Emma Hart

  3. Edinburgh Napier University, Edinburgh, UK

    Sarah L. Thomson

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Grazioso, M., Gallese, C., Vanneschi, L., Nobile, M.S. (2025). A Survey of Modern Hybrid Particle Swarm Optimization Algorithms. In: García-Sánchez, P., Hart, E., Thomson, S.L. (eds) Applications of Evolutionary Computation. EvoApplications 2025. Lecture Notes in Computer Science, vol 15613. Springer, Cham. https://doi.org/10.1007/978-3-031-90065-5_7

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