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Yazdani, D, Yazdani, D, Yazdani, D, Omidvar, MN, Gandomi, AH and Yao, X (2023) A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems. ACM Transactions on Evolutionary Learning and Optimization, 3 (4). pp. 1-25. ISSN 2688-299X
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A Species-based Particle Swarm Optimization with Adaptive Population Size and Deactivation of Species for Dynamic Optimization Problems.pdf - Published Version Available under License Creative Commons Attribution Non-commercial Share Alike. Download (1MB) | Preview |
Abstract
Population clustering methods, which consider the position and fitness of individuals to form sub-populations in multi-population algorithms, have shown high efficiency in tracking the moving global optimum in dynamic optimization problems. However, most of these methods use a fixed population size, making them inflexible and inefficient when the number of promising regions is unknown. The lack of a functional relationship between the population size and the number of promising regions significantly degrades performance and limits an algorithm’s agility to respond to dynamic changes. To address this issue, we propose a new species-based particle swarm optimization with adaptive population size and number of sub-populations for solving dynamic optimization problems. The proposed algorithm also benefits from a novel systematic adaptive deactivation component that, unlike the previous deactivation components, adapts the computational resource allocation to the sub-populations by considering various characteristics of both the problem and the sub-populations. We evaluate the performance of our proposed algorithm for the Generalized Moving Peaks Benchmark and compare the results with several peer approaches. The results indicate the superiority of the proposed method.
| Item Type: | Article |
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| Subjects: | T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Engineering |
| Publisher: | Association for Computing Machinery (ACM) |
| SWORD Depositor: | A Symplectic |
| Date Deposited: | 30 Jul 2024 10:30 |
| Last Modified: | 30 Jul 2024 10:30 |
| DOI or ID number: | 10.1145/3604812 |
| URI: | https://researchonline.ljmu.ac.uk/id/eprint/23822 |
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