<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></abstract>

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.

A New Auto Adaptive Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

The social learning mechanism used in the Particle Swarm Optimization algorithm allows this method to converge quickly. However, it can lead to catching the...

A New Auto Adaptive Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm

Publié en ligne: 20 mars 2020

Pages: 95 - 111

Reçu: 29 sept. 2019

Accepté: 21 févr. 2020

DOI: https://doi.org/10.2478/jaiscr-2020-0007

Mots clés
hybrid methods, Particle Swarm Optimization, Genetic Algorithm, fuzzy systems, multimodal functions

© 2020 Piotr Dziwiński et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

A New Auto Adaptive Fuzzy Hybrid Particle Swarm Optimization and Genetic Algorithm

Publié en ligne: 20 mars 2020

Pages: 95 - 111

Reçu: 29 sept. 2019

Accepté: 21 févr. 2020

DOI: https://doi.org/10.2478/jaiscr-2020-0007

Mots cléshybrid methods, Particle Swarm Optimization, Genetic Algorithm, fuzzy systems, multimodal functions

© 2020 Piotr Dziwiński et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.

Mots clés
hybrid methods, Particle Swarm Optimization, Genetic Algorithm, fuzzy systems, multimodal functions