An Enhanced Differential Evolution Algorithm Using a Novel Clustering-based Mutation Operator
Seyed Jalaleddin Mousavirad, Gerald Schaefer, Iakov Korovin, Mahshid Helali Moghadam, Mehrdad Saadatmand, Mahdi Pedram
Abstract
Differential evolution (DE) is an effective population-based metaheuristic algorithm for solving complex optimisation problems. However, the performance of DE is sensitive to the mutation operator. In this paper, we propose a novel DE algorithm, Clu-DE, that improves the efficacy of DE using a novel clustering-based mutation operator. First, we find, using a clustering algorithm, a winner cluster in search space and select the best candidate solution in this cluster as the base vector in the mutation operator. Then, an updating scheme is introduced to include new candidate solutions in the current population. Experimental results on CEC-2017 benchmark functions with dimensionalities of 30, 50 and 100 confirm that Clu-DE yields improved performance compared to DE.