Improving Clustering-based Differential Evolution with Chaotic Sequences and New Mutation Operator

Abstract

Differential evolution (DE) algorithms compose an efficient type of evolutionary algorithm (EA) for the global optimization domain. But DE is not completely free from the problems of slow and/or premature convergence. In this paper, an improving clustering-based differential evolution with chaotic sequences and new mutation operator (CCDE) is proposed for the unconstrained global optimization problems. In CCDE, the population is partitioned into k subsets by a clustering algorithm, and each subset is considered to be the cluster neighborhood. And these cluster centers and chaotic sequences using logistic equation are used to design the new differential evolution mutation operators. This method utilizes the concept of the cluster neighborhood of each population member. The CEC2005 benchmark functions are employed for experimental verification. Experimental results indicate that CCDE is highly competitive compared to the state-of-the-art DE algorithms.