# An Enhanced Differential Evolution Algorithm Using a Novel Clustering-based Mutation Operator

arXiv: https://arxiv.org/abs/2109.09351 · HTML: https://mlsanity.com/paper/2109.09351/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Sep 2021.

## Built on by (most cited first, 1 of 1)

- [The importance of being constrained: dealing with infeasible solutions in Differential Evolution and beyond](https://mlsanity.com/paper/2203.03512/) (2022)

## Abstract (arXiv)

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.