survey and experimental analysis of the differential evolution algorithm

Abstract

The differential evolution algorithm, since its introduction in 1997, has spread widely in various engineering and applied fields. It is one of the metaheurism search algorithms used for optimal decision-making processes. In view of the valuable additions made by this algorithm in the search for solutions to complex models, it was useful to provide a review of this algorithm and the modifications and additions that have been made to it in order to increase the efficiency of use and raise the level of performance. This paper presents an updated review of differential evaluation and suggested modifications to enhance the effectiveness and efficiency of the original evaluation by analyzing the strengths and weaknesses of several valuable articles and works published. Additional analyzes are performed in this survey by investigating the effects of different parameter settings on DE variables to determine the optimal values required to solve a particular problem. The properties of the modifications included in the selected DE variables also evaluated by measuring the performance gains achieved in terms of research accuracy and efficiency against the original DE.