metaheuristics, multi-objective optimization, wolf optimizer, fuzzy systems, Pareto
Abstract
The Multi-Objective Grey Wolf Optimizer (MOGWO) is a recent metaheuristic in solving multi-objective problems. This article implements two mechanisms for adjusting the control parameters of the MOGWO. The techniques are based on fuzzy logic and were originally proposed to improve the convergence of the single-objective version of the MOGWO. To evaluate the two fuzzy implementations, five multiobjective test problems were used, where each algorithm was submitted to a sequence of ten runs. The inverted generational distance was used as a metric for the comparison and evaluation of the algorithms. The results show that the two fuzzy mechanisms improve the convergence of the MOGWO.
