Affiliations: [a] Centro Universitário UniSociesc, Curitiba, Brazil
| [b] Department of Electrical Engineering, Electrical Engineering Graduate Program, Federal University of Paraná, Curitiba, Brazil
| [c] Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Paraná, Curitiba, Brazil
Correspondence:
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Corresponding author. Allan Christian Krainski Ferrari, Professor at Centro Universitário UniSociesc, Curitiba, Brazil. E-mail: [email protected].
Abstract: The Whale Optimization Algorithm (WOA) is a recent meta-heuristic that can be explored in global optimization problems. This paper proposes a new parameter adjustment mechanism that influences the probability of the food recognition process in the whale algorithm. The adjustment is performed using a fuzzy inference system that uses the current iteration number as input information. Our simulation results are compared with other meta-heuristics such as the conventional version of WOA, Particle Swarm Optimization (PSO) and Differential Evolution (DE). All algorithms are used to optimize ten test functions (Sphere, Schwefel 2.22, Quartic, Rosenbrock, Ackley, Rastrigin, Penalty 1, Schwefel 2.21, Six hump camel back and Shekel 1) in order to obtain their respective optimal values for be used as criteria for analysis and comparison. The results of the simulations show that the proposed fuzzy inference system improves the convergence of WOA and also is competitive in relation to the other algorithms, i.e., classical WOA, PSO and DE.
