Subspace Clustering Mutation Operator for Developing Convergent Differential Evolution Algorithm

A Memetic Differential Evolution Algorithm with Adaptive Mutation Operator

Differential evolution algorithm has been widely used, because of its efficient optimization and no complex operation and coding mechanism. But DE falls into the local optimum easily. So this study puts forward a memetic algorithm. The algorithm can increase the diversity of population and jump out the local extreme value point effectively. The convergence speed of the algorithm is improved, be...

Differential Evolution with Generalized Mutation Operator

Differential Evolution (DE) proved to be one of the most successful evolutionary algorithms for global optimization purposes in continuous problems. The core operator in DE is mutation which can provide the algorithm with both exploration and exploitation. In this article, a new notation for DE is proposed which has a formula that can be utilized for generating and extracting novel mutations an...

Hybrid differential evolution algorithm for optimal clustering

The problem of optimal non-hierarchical clustering is addressed. A new algorithm combining differential evolution and k-means is proposed and tested on eight well-known real-world data sets. Two criteria (clustering validity indexes), namely TRW and VCR, were used in the optimization of classification. The classification of objects to be optimized is encoded by the cluster centers in differenti...

Optimized Analysis Based on Improved Mutation and Crossover Operator for Differential Evolution Algorithm

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

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 p...