Guilbault, Raynald.
2025.
« S-EPSO: A socio-emotional particle swarm optimization algorithm for multimodal search in low-dimensional engineering applications ».
Algorithms, vol. 18, nº 6.
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Résumé
This paper examines strategies aimed at improving search procedures in multimodal, low-dimensional domains. Here, low-dimensional domains refers to a maximum of five dimensions. The present analysis assembles strategies to form an algorithm named S-EPSO, which, at its core, locates and maintains multiple optima without relying on external niching parameters, instead adapting this functionality internally. The first proposed strategy assigns socio-emotional personalities to the particles forming the swarm. The analysis also introduces a technique to help them visit secluded zones. It allocates the particles of the initial distribution to subdomains based on biased decisions. The biases reflect the subdomain’s potential to contain optima. This potential is established from a balanced combination of the jaggedness and the mean-average interval descriptors developed in the study. The study compares the performance of S-EPSO to that of state-of-the-art algorithms over seventeen functions of the CEC benchmark, and S-EPSO is revealed to be highly competitive. It outperformed the reference algorithms 14 times, whereas the best of the latter outperformed the other two 10 times out of 30 relevant evaluations. S-EPSO performed best with the most challenging 5D functions of the benchmark. These results clearly illustrate the potential of S-EPSO when it comes to dealing with practical engineering optimization problems limited to five dimensions.
| Type de document: | Article publié dans une revue, révisé par les pairs |
|---|---|
| Professeur: | Professeur Guilbault, Raynald |
| Affiliation: | Génie mécanique |
| Date de dépôt: | 30 juill. 2025 13:26 |
| Dernière modification: | 11 août 2025 22:27 |
| URI: | https://espace2.etsmtl.ca/id/eprint/31207 |
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