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A review of optimization swarm intelligence-inspired algorithms with type-2 fuzzy logic parameter adaptation

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Abstract

In this paper, a survey about the algorithms based on swarm intelligence with parameter adaptation using some techniques to achieve the best results is presented. In this case, we analyzed the most popular algorithms such as ant colony optimization, particle swarm optimization, bee colony optimization, bat algorithm, firefly algorithm and cuckoo search. These algorithms are referenced in the paper because they have demonstrated to be superior with respect to the other optimization methods based on swarms with parameter adaptation using type-2 fuzzy logic in some applications, and also the algorithms are inspired on swarm intelligence.

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Acknowledgements

The author would like to thank CONACYT and Tecnológico Nacional de Mexico/Tijuana Institute of Technology for the support during this research work.

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  1. Tijuana Institute of Technology/Tecnológico Nacional de México, Tijuana, BC, Mexico

    Fevrier Valdez

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Fevrier Valdez worked on the conceptualization and proposal of the methodology, on the formal analysis, writing and reviewing the paper, on the investigation and validation of the results.

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Correspondence to Fevrier Valdez.

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Communicated by O. Castillo. D. K. Jana.

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Valdez, F. A review of optimization swarm intelligence-inspired algorithms with type-2 fuzzy logic parameter adaptation. Soft Comput 24, 215–226 (2020). https://doi.org/10.1007/s00500-019-04290-y

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