Szczegóły publikacji
Opis bibliograficzny
Sequential, parallel and consecutive hybrid evolutionary-swarm optimization metaheuristics / Piotr URBAŃCZYK, Aleksandra URBAŃCZYK, Magdalena KRÓL, Leszek Rutkowski, Marek KISIEL-DOROHINICKI // 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. 203–218. — Bibliogr., Abstr. — Publikacja dostępna online od: 2025-07-07. — P. Urbańczyk - dod. afiliacja: Jagiellonian University ; L. Rutkowski - afiliacja: Polish Academy of Sciences, Warsaw
Autorzy (5)
Dane bibliometryczne
| ID BaDAP | 161027 |
|---|---|
| Data dodania do BaDAP | 2025-07-18 |
| DOI | 10.1007/978-3-031-97554-7_15 |
| 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
The goal of this paper is twofold. First, it explores hybrid evolutionary-swarm metaheuristics that combine the features of PSO and GA in a sequential, parallel and consecutive manner in comparison with their standard basic form: Genetic Algorithm and Particle Swarm Optimization. The algorithms were tested on a set of benchmark functions, including Ackley, Griewank, Levy, Michalewicz, Rastrigin, Schwefel, and Shifted Rotated Weierstrass, across multiple dimensions. The experimental results demonstrate that the hybrid approaches achieve superior convergence and consistency, especially in higher-dimensional search spaces. The second goal of this paper is to introduce a novel consecutive hybrid PSO-GA evolutionary algorithm that ensures continuity between PSO and GA steps through explicit information transfer mechanisms, specifically by modifying GA’s variation operators to inherit velocity and personal best information.
