Proposed of Fuzzy Parametric Adaptation of the Acceleration Coefficients and Inertial Weight in GA-PSO Hybridization

Resumen: This paper aims to propose a new hybridization involving the Genetic Algorithm (GA) and the Particle Optimization Swarm (PSO). The objective of the proposed optimization algorithm is to perform the search process for optimal solutions in complex problems with a fast and non-premature convergence. Since the satisfactory convergence of the search process is a result of a good trade-off between global and local search, in order to achieve the objective of the proposed optimization algorithm, a Mamdani Fuzzy Inference System (MFIS) is used for fuzzy parametric adaptation of the acceleration coefficients and inertial weight of PSO. Through this parametric adaptation, which is performed using a linguistic description based on the expert's knowledge and implemented in a fuzzy rule base, it is possible to obtain a good trade-off between global and local search in complex optimization problems.

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Possidônio Noronha, Rodrigo. Proposed of Fuzzy Parametric Adaptation of the Acceleration Coefficients and Inertial Weight in GA-PSO Hybridization. Memorias del Congreso Nacional de Control Automático, pp. 148-153, 2021.

Palabras clave

Genetic Algorithm, Evolutionary Computation, Hybridization, Particle Swarm Optimization, Fuzzy System

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