A clustering-based differential evolution for global optimization

  • Authors:

  • Zhihua Cai;Wenyin Gong;Charles X. Ling;Harry Zhang

  • Affiliations:

  • School of Computer Science, China University of Geosciences, Wuhan 430074, PR China;School of Computer Science, China University of Geosciences, Wuhan 430074, PR China and Department of Computer Science, The University of Western Ontario, London, Ontario, N6A 5B7 Canada;Department of Computer Science, The University of Western Ontario, London, Ontario, N6A 5B7 Canada;School of Computer Science, The University of New Brunswick, Fredrecton 4000, Canada

  • Venue:
  • Applied Soft Computing
  • Year:
  • 2011

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Abstract

Hybridization with other different algorithms is an interesting direction for the improvement of differential evolution (DE). In this paper, a hybrid DE based on the one-step k-means clustering, called clustering-based DE (CDE), is presented for the unconstrained global optimization problems. The one-step k-means clustering acts as several multi-parent crossover operators to utilize the information of the population efficiently, and hence it can enhance the performance of DE. To validate the performance of our approach, 30 benchmark functions of a wide range of dimensions and diversity complexities are employed. Experimental results indicate that our approach is effective and efficient. Compared with other state-of-the-art DE approaches, our approach performs better, or at least comparably, in terms of the quality of the final solutions and the reduction of the number of fitness function evaluations (NFFEs).