An Enhanced Differential Evolution with Elite Chaotic Local Search.

Journal: Computational intelligence and neuroscience
Published Date: Aug 24, 2015

Abstract

Differential evolution (DE) is a simple yet efficient evolutionary algorithm for real-world engineering problems. However, its search ability should be further enhanced to obtain better solutions when DE is applied to solve complex optimization problems. This paper presents an enhanced differential evolution with elite chaotic local search (DEECL). In DEECL, it utilizes a chaotic search strategy based on the heuristic information from the elite individuals to promote the exploitation power. Moreover, DEECL employs a simple and effective parameter adaptation mechanism to enhance the robustness. Experiments are conducted on a set of classical test functions. The experimental results show that DEECL is very competitive on the majority of the test functions.

Authors

  • Zhaolu Guo
    Institute of Medical Informatics and Engineering, School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China.
  • Haixia Huang
    School of Literature and Law, Jiangxi University of Science and Technology, Ganzhou 341000, China.
  • Changshou Deng
    School of Information Science and Technology, Jiujiang University, Jiujiang 332005, China.
  • Xuezhi Yue
    Institute of Medical Informatics and Engineering, School of Science, Jiangxi University of Science and Technology, Ganzhou 341000, China.
  • Zhijian Wu
    State Key Laboratory of Software Engineering, Wuhan University, Wuhan 430072, China.

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