A new Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation

Marylu Lagunes; Oscar Castillo; Fevrier Valdez; José Soria; Patricia Melin

doi:10.20944/preprints202103.0668.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 25 March 2021 / Approved: 26 March 2021 / Online: 26 March 2021 (14:17:13 CET)

Metaheuristic algorithms are widely used as optimization methods, due to their global exploration and exploitation characteristics, which obtain better results than a simple heuristic. The Stochastic Fractal Search (SFS) is a novel method inspired by the process of stochastic growth in nature and the use of the fractal mathematical concept. Considering the chaotic-stochastic diffusion property, an improved Dynamic Stochastic Fractal Search (DSFS) optimization algorithm is presented. The DSFS algorithm was tested with benchmark functions, such as the multimodal, hybrid and composite functions, to evaluate the performance of the algorithm with dynamic parameter adaptation with type-1 and type-2 fuzzy inference models. The main contribution of the article is the utilization of fuzzy logic in the adaptation of the diffusion parameter in a dynamic fashion. This parameter is in charge of creating new fractal particles, and the diversity and iteration are the input information used in the fuzzy system to control the values of diffusion.

Keywords: Fractal Search; Fuzzy Logic; Parameter Adaptation; CEC´2017.

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A new Approach for Dynamic Stochastic Fractal Search with Fuzzy Logic for Parameter Adaptation

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