Szczegóły publikacji
Opis bibliograficzny
Structural limiting range of perception in particle swarm optimization / Mateusz MASTALERCZYK, Małgorzata ZAJĘCKA, Sylwia BIEŁASZEK, Marek KISIEL-DOROHINICKI, Aleksander BYRSKI // W: Computational Science – ICCS 2025 Workshops : 25th international conference : Singapore, Singapore, July 7–9, 2025 : proceedings, Pt. 1 / eds. Maciej Paszyński, Amanda S. Barnard, Yongjie Jessica Zhang. — Cham : Springer Nature Switzerland, cop. 2025. — (Lecture Notes in Computer Science ; ISSN 0302-9743 ; LNCS 15907). — ISBN: 978-3-031-97553-0; e-ISBN: 978-3-031-97554-7. — S. 265–279. — Bibliogr., Abstr. — Publikacja dostępna online od: 2025-07-07
Autorzy (5)
Dane bibliometryczne
| ID BaDAP | 161032 |
|---|---|
| Data dodania do BaDAP | 2025-08-07 |
| DOI | 10.1007/978-3-031-97554-7_19 |
| Rok publikacji | 2025 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | International Conference on Computational Science 2025 |
| Czasopismo/seria | Lecture Notes in Computer Science |
Abstract
This research explores the impact of structural limiting perception on the performance of Particle Swarm Optimization by restricting the range of information sharing among particles. By introducing localized communication models through Ring and Tree topologies, the study demonstrates significant improvements over the standard global-best PSO, particularly on a range of Traveling Salesman Problem instances from the TSPLIB. The results show that constraining particle perception enhances both solution quality and convergence behavior, with the Tree topology emerging as the most effective structure. The topological modifications maintain swarm diversity, prevent premature convergence, and facilitate continuous exploration while exploiting promising search regions. These findings suggest that structural constraints on information sharing can enhance PSO’s robustness and effectiveness without adding computational complexity, offering a flexible approach applicable to various PSO variants and problem domains beyond TSP.
