A comparative study on the performance of Gray Wolves Optimization and Multi-Free Dynamic Schema

Abstract

The most difficult step is to create new optimization algorithms and examine them using test functions. In this work, we present a comparison study on the performance of Gray Wolves Optimization (GWO) and Multi-free dynamic schema (MFDS) algorithms. The (MFDS) algorithm is a sophisticated optimization method created for solving optimization problems. It contains different operators (dynamic schema operator, dissimilarity operator, similarity operator and free dynamic schema operator). Where, The (GWO) is a meta-heuristic optimization algorithm inspired by the social behavior of grey wolves in a pack. This study focused on the run time and the number of iterations to reach the optimal solutions. The sample of this comparison was on ten functions. The results showed the superiority of an algorithm (MFDS) on (GWO) algorithm in most test functions, especially at the run time. The performance of any single-objective optimization algorithm is a tool to measures the effectiveness of any algorithm for determining out the best solution for a specific problem.