An effective memetic differential evolution algorithm based on chaotic local search

  • Authors:

  • Dongli Jia;Guoxin Zheng;Muhammad Khurram Khan

  • Affiliations:

  • Key Laboratory of Special Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200072, China and School of Information and Electronic Engineering, Hebei University of Engineerin ...;Key Laboratory of Special Fiber Optics and Optical Access Networks, Shanghai University, Shanghai 200072, China;Center of Excellence in Information Assurance (CoEIA), King Saud University, Riyadh 11653, Saudi Arabia

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2011

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Abstract

This paper proposes an effective memetic differential evolution (DE) algorithm, or DECLS, that utilizes a chaotic local search (CLS) with a 'shrinking' strategy. The CLS helps to improve the optimizing performance of the canonical DE by exploring a huge search space in the early run phase to avoid premature convergence, and exploiting a small region in the later run phase to refine the final solutions. Moreover, the parameter settings of the DECLS are controlled in an adaptive manner to further enhance the search ability. To evaluate the effectiveness and efficiency of the proposed DECLS algorithm, we compared it with four state-of-the-art DE variants and the IPOP-CMA-ES algorithm on a set of 20 selected benchmark functions. Results show that the DECLS is significantly better than, or at least comparable to, the other optimizers in terms of convergence performance and solution accuracy. Besides, the DECLS has also shown certain advantages in solving high dimensional problems.