An extension for differential evolution based on centroids with self-adaptive noise and preselection

Abstract

Continuous optimisation in different technical systems is an ever ongoing, but in many cases a challenging task. Evolutionary algorithms and, in particular, differential evolution (DE) have shown their potential as effective means of optimisation in numerous such instances, especially when the systems are “black boxes”. Yet, in an endeavour to increase the performance of these algorithms even further, various modifications and extensions are steadily introduced. This paper is just another step in the pursuit of gaining additional performance. Correspondingly, a DE extension, one that might benefit not only the canonical algorithm, but also already modified or extended varieties is proposed to this end. The proposal features a straightforward incorporation into the algorithm and is based on the preselection, via a simple surrogate model, of multiple candidate centroids of the entire population that are infused with self-adaptive normal noise. The soundness and viability of its design have been shown experimentally by incorporating it into different DE variants and evaluating the performance gains on a diverse set of challenging benchmark functions.