Kontaktujte nás | Jazyk: čeština English
Název: | Particle swarm optimization with single particle repulsivity for multi-modal optimization | ||||||||||
Autor: | Pluháček, Michal; Šenkeřík, Roman; Viktorin, Adam; Kadavý, Tomáš | ||||||||||
Typ dokumentu: | Článek ve sborníku (English) | ||||||||||
Zdrojový dok.: | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2018, vol. 10841 LNAI, p. 486-494 | ||||||||||
ISSN: | 0302-9743 (Sherpa/RoMEO, JCR) | ||||||||||
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ISBN: | 978-3-319-91252-3 | ||||||||||
DOI: | https://doi.org/10.1007/978-3-319-91253-0_45 | ||||||||||
Abstrakt: | This work presents a simple but effective modification of the velocity updating formula in the Particle Swarm Optimization algorithm to improve the performance of the algorithm on multi-modal problems. The well-known issue of premature swarm convergence is addressed by a repulsive mechanism implemented on a single-particle level where each particle in the population is partially repulsed from a different particle. This mechanism manages to prolong the exploration phase and helps to avoid many local optima. The method is tested on well-known and typically used benchmark functions, and the results are further tested for statistical significance. © Springer International Publishing AG, part of Springer Nature 2018. | ||||||||||
Plný text: | https://link.springer.com/chapter/10.1007/978-3-319-91253-0_45 | ||||||||||
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