An Enhanced Differential Evolution Algorithm Using a Novel Clustering-based Mutation Operator
This work addresses optimization problems in metaheuristic algorithms, but it is incremental as it builds on existing DE methods.
The paper tackled the sensitivity of differential evolution (DE) to mutation operators by proposing Clu-DE, a novel algorithm using a clustering-based mutation operator, which improved performance on CEC-2017 benchmark functions across dimensions of 30, 50, and 100.
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.