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Erschienen in: International Journal of Machine Learning and Cybernetics 6/2019

24.03.2018 | Original Article

A collaboration-based particle swarm optimizer for global optimization problems

verfasst von: Leilei Cao, Lihong Xu, Erik D. Goodman

Erschienen in: International Journal of Machine Learning and Cybernetics | Ausgabe 6/2019

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Abstract

This paper introduces a collaboration-based particle swarm optimizer (PSO) by incorporating three new strategies: a global learning strategy, a probability of learning, and a “worst replacement” swarm update rule. Instead of learning from the personal historical best position and the global (or local) best position which was used by the classical PSO, a target particle learns from another randomly chosen particle and the global best one in the swarm. Instead of accepting a new velocity directly, the velocity updates according to a learning probability, according to which the velocity of the target particle in each dimension updates via learning from other particles or simply inherits its previous velocity component. Since each particle has the same chance to be selected as a leader, the worst particle might influence the whole swarm’s performance. Therefore, the worst particle in the swarm in each update is moved to a new better position generated from another particle. The proposed algorithm is shown to be statistically significantly better than six other state-of-the-art PSO variants on 20 typical benchmark functions with three different dimensionalities.

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Metadaten
Titel
A collaboration-based particle swarm optimizer for global optimization problems
verfasst von
Leilei Cao
Lihong Xu
Erik D. Goodman
Publikationsdatum
24.03.2018
Verlag
Springer Berlin Heidelberg
Erschienen in
International Journal of Machine Learning and Cybernetics / Ausgabe 6/2019
Print ISSN: 1868-8071
Elektronische ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-018-0810-0

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