Publication
A Survey of Modern Hybrid Particle Swarm Optimization Algorithms
| Summary: | 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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| Main Authors: | Grazioso, Matteo |
| Other Authors: | Gallese, Chiara; Vanneschi, Leonardo; Nobile, Marco S. |
| Subject: | Particle Swarm Optimization Hybrid algorithms Global optimization meta-heuristics Theoretical Computer Science General Computer Science |
| Year: | 2025 |
| Country: | Portugal |
| Document type: | conference output |
| Access type: | open access |
| Associated institution: | Universidade Nova de Lisboa |
| Language: | English |
| Origin: | Repositório Institucional da UNL |
| Summary: | 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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