Dynamic simultaneous adaptation of parameters in the grey wolf optimizer using fuzzy logic

The main goal of the work presented in the paper is to introduce the use of fuzzy logic in the Grey Wolf Optimizer (GWO) algorithm specifically for dynamic simultaneous adaptation of the key parameters, which are crucial in the performance of the metaheuristic. The proposed approach for this modification of GWO using fuzzy logic is presented. In addition, a brief comparison between the traditional GWO algorithm and the Grey Wolf Optimizer using fuzzy logic for dynamic adaptation of parameters is reported. This research shows the individual dynamic adjustment of two parameters and then a proposal of how to simultaneously adjust both parameters and finally we present the performance of these methods when they are tested with a set of benchmark functions, showing the advantage of using the strategy of simultaneous adaptation of parameters.