An Enhanced Differential Evolution Algorithm Using a Novel Clustering-based Mutation Operator
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.
Keywords
Cite
@article{arxiv.2109.09351,
title = {An Enhanced Differential Evolution Algorithm Using a Novel Clustering-based Mutation Operator},
author = {Seyed Jalaleddin Mousavirad and Gerald Schaefer and Iakov Korovin and Mahshid Helali Moghadam and Mehrdad Saadatmand and Mahdi Pedram},
journal= {arXiv preprint arXiv:2109.09351},
year = {2021}
}
Comments
6 pages, IEEE International Conference on Systems, Man, and Cybernetics (SMC 2021)