Development and analysis of a self-configuring differential evolution algorithm

Abstract

In this study to solve optimization problem, three differential evolution algorithms are tested on various functions, highlighting its parameter sensitivity. To overcome this, a self-configuring algorithm is introduced, which core idea is to periodically reevaluate configurations, favoring those with superior performance. Self-configuring algorithms in most cases outperform or match conventional methods, enhancing the likelihood of achieving superior results.