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Title: | Multiple choice strategy for PSO algorithm enhanced with dimensional mutation | ||||||||||
Author: | Pluháček, Michal; Šenkeřík, Roman; Zelinka, Ivan; Davendra, Donald David | ||||||||||
Document type: | Conference paper (English) | ||||||||||
Source document: | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 2015, vol. 9119, p. 370-378 | ||||||||||
ISSN: | 0302-9743 (Sherpa/RoMEO, JCR) | ||||||||||
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ISBN: | 978-3-319-19323-6 | ||||||||||
DOI: | https://doi.org/10.1007/978-3-319-19324-3-34 | ||||||||||
Abstract: | In this study the promising Multiple-choice strategy for PSO (MC-PSO) is enhanced with the blind search based single dimensional mutation. The MC-PSO utilizes principles of heterogeneous swarms with random behavior selection. The performance previously tested on both large-scale and fast optimization is significantly improved by this approach. The newly proposed algorithm is more robust and resilient to premature convergence than both original PSO and MC-PSO. The performance is tested on four typical benchmark functions with variety of dimension settings. © Springer International Publishing Switzerland 2015. | ||||||||||
Full text: | https://link.springer.com/chapter/10.1007/978-3-319-19324-3_34 | ||||||||||
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