Numerical Analysis of The Differential Evolution Optimization Algorithm and Its Control Parameters

Abstract

This thesis aims to investigate the impact of control parameters and variants of the DE algorithm on its performance. By analyzing parameters like population size and mutation rates, alongside exploring various DE algorithm variants, the study aims to understand their combined influence on algorithm efficiency. Moreover, this research has the potential to be applied to various fields requiring optimization, including Maximum Power Point Tracking (MPPT).