A new simplified MOPSO based on Swarm Elitism and Swarm Memory: MO-ETPSO
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866914686205689856 |
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| author | Fitas, Ricardo |
| author_facet | Fitas, Ricardo |
| contents | This paper presents an algorithm based on Particle Swarm Optimization (PSO), adapted for multi-objective optimization problems: the Elitist PSO (MO-ETPSO). The proposed algorithm integrates core strategies from the well-established NSGA-II approach, such as the Crowding Distance Algorithm, while leveraging the advantages of Swarm Intelligence in terms of individual and social cognition. A novel aspect of the algorithm is the introduction of a swarm memory and swarm elitism, which may turn the adoption of NSGA-II strategies in PSO. These features enhance the algorithm's ability to retain and utilize high-quality solutions throughout optimization. Furthermore, all operators within the algorithm are intentionally designed for simplicity, ensuring ease of replication and implementation in various settings. Preliminary comparisons with the NSGA-II algorithm for the Green Vehicle Routing Problem, both in terms of solutions found and convergence, have yielded promising results in favor of MO-ETPSO. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2402_12856 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A new simplified MOPSO based on Swarm Elitism and Swarm Memory: MO-ETPSO Fitas, Ricardo Neural and Evolutionary Computing This paper presents an algorithm based on Particle Swarm Optimization (PSO), adapted for multi-objective optimization problems: the Elitist PSO (MO-ETPSO). The proposed algorithm integrates core strategies from the well-established NSGA-II approach, such as the Crowding Distance Algorithm, while leveraging the advantages of Swarm Intelligence in terms of individual and social cognition. A novel aspect of the algorithm is the introduction of a swarm memory and swarm elitism, which may turn the adoption of NSGA-II strategies in PSO. These features enhance the algorithm's ability to retain and utilize high-quality solutions throughout optimization. Furthermore, all operators within the algorithm are intentionally designed for simplicity, ensuring ease of replication and implementation in various settings. Preliminary comparisons with the NSGA-II algorithm for the Green Vehicle Routing Problem, both in terms of solutions found and convergence, have yielded promising results in favor of MO-ETPSO. |
| title | A new simplified MOPSO based on Swarm Elitism and Swarm Memory: MO-ETPSO |
| topic | Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2402.12856 |