A new auto adaptive fuzzy hybrid particle swarm optimization and genetic algorithm

• Autorzy: Dziwiński Piotr , Bartczuk Łukasz , Paszkowski Józef

Identyfikatory

DOI

10.2478/jaiscr-2020-0007

• Języki publikacji: EN

Abstrakty

EN

The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the swarm in the local optimum. The solution to this issue may be the use of genetic operators whose random nature allows them to leave this point. The degree of use of these operators can be controlled using a neuro-fuzzy system. Previous studies have shown that the form of fuzzy rules should be adapted to the fitness landscape of the problem. This may suggest that in the case of complex optimization problems, the use of different systems at different stages of the algorithm will allow to achieve better results. In this paper, we introduce an auto adaptation mechanism that allows to change the form of fuzzy rules when solving the optimization problem. The proposed mechanism has been tested on benchmark functions widely adapted in the literature. The results verify the effectiveness and efficiency of this solution.

Słowa kluczowe

EN

hybrid methods; Particle Swarm Optimization; genetic algorithm; fuzzy systems; multimodal function

• Wydawca: University of Social Sciences
• Czasopismo: Journal of Artificial Intelligence and Soft Computing Research
• Rocznik: 2020
• Tom: Vol. 10, No. 2
• Strony: 95--111
• Opis fizyczny: Bibliogr. 38 poz., rys.

Twórcy

autor

Dziwiński Piotr

• Department of Computational Intelligence, Czestochowa University of Technology, al. Armii Krajowej 36, 42-200 Częstochowa, Poland

autor

Bartczuk Łukasz

• Department of Computational Intelligence, Czestochowa University of Technology, al. Armii Krajowej 36, 42-200 Częstochowa, Poland

autor

Paszkowski Józef

• Information Technology Institute, University of Social Sciences, 90-113 Łódź
• Clark University Worcester, MA 01610, USA

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).