Improved Differential Evolution with Mahalanobis Distance: IMPDE
Ángel Casas-Ordaz, Mario A. Navarro, Arturo Valdivia, Diego A. Oliva, Jorge Armando Ramos-Frutos · 2025
It is an irrefutable fact that the Differential Evolution (DE) algorithm is one of the most widely used stochastic algorithms for solving complex optimization problems. In recent decades, the DE algorithm has garnered significant interest from researchers due to its considerable potential. However, it is evident that the scientific community considers it necessary to modify and create variants of the original version to improve its performance. Within the DE algorithm, the mutation operator has been an essential component in improving performance and exploration/exploitation balance. The present manuscript proposes a novel approach to the mutation operator of the DE algorithm, grounded in the Mahalanobis distance. The Mahalanobis Distance Enhanced Differential Evolution (IMPDE) algorithm is an innovative methodology that aims to explore a new paradigm by measuring the distance of particles to the mean during each iteration. The enhancement of the algorithm is appraised by employing a series of CEC-2017 assessments and traditional optimization problems. Furthermore, a nonparametric Friedman test is conducted to refute the obtained results. This work represents a significant contribution to the ongoing efforts to enhance the optimization process of one of the most prevalent algorithms in recent times.