Learning Strategies in Particle Swarm Optimizer: A Critical Review and Performance Analysis
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866911269309644800 |
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| author | Chauhan, Dikshit Shivani Suganthan, P. N. |
| author_facet | Chauhan, Dikshit Shivani Suganthan, P. N. |
| contents | Nature has long inspired the development of swarm intelligence (SI), a key branch of artificial intelligence that models collective behaviors observed in biological systems for solving complex optimization problems. Particle swarm optimization (PSO) is widely adopted among SI algorithms due to its simplicity and efficiency. Despite numerous learning strategies proposed to enhance PSO's performance in terms of convergence speed, robustness, and adaptability, no comprehensive and systematic analysis of these strategies exists. We review and classify various learning strategies to address this gap, assessing their impact on optimization performance. Additionally, a comparative experimental evaluation is conducted to examine how these strategies influence PSO's search dynamics. Finally, we discuss open challenges and future directions, emphasizing the need for self-adaptive, intelligent PSO variants capable of addressing increasingly complex real-world problems. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2504_11812 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Learning Strategies in Particle Swarm Optimizer: A Critical Review and Performance Analysis Chauhan, Dikshit Shivani Suganthan, P. N. Neural and Evolutionary Computing Artificial Intelligence Nature has long inspired the development of swarm intelligence (SI), a key branch of artificial intelligence that models collective behaviors observed in biological systems for solving complex optimization problems. Particle swarm optimization (PSO) is widely adopted among SI algorithms due to its simplicity and efficiency. Despite numerous learning strategies proposed to enhance PSO's performance in terms of convergence speed, robustness, and adaptability, no comprehensive and systematic analysis of these strategies exists. We review and classify various learning strategies to address this gap, assessing their impact on optimization performance. Additionally, a comparative experimental evaluation is conducted to examine how these strategies influence PSO's search dynamics. Finally, we discuss open challenges and future directions, emphasizing the need for self-adaptive, intelligent PSO variants capable of addressing increasingly complex real-world problems. |
| title | Learning Strategies in Particle Swarm Optimizer: A Critical Review and Performance Analysis |
| topic | Neural and Evolutionary Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2504.11812 |