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Ensemble of Clearing Differential Evolution for Multi-modal Optimization

  • Boyang Qu
  • Jing Liang
  • Ponnuthurai Nagaratnam Suganthan
  • Tiejun Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7331)

Abstract

Multi-modal Optimization refers to finding multiple global and local optima of a function in one single run, so that the user can have a better knowledge about different optimal solutions. Multiple global/local peaks generate extra difficulties for the optimization algorithms. Many niching techniques have been developed in literature to tackle multi-modal optimization problems. Clearing is one of the simplest and most effective methods in solving multi-modal optimization problems. In this work, an Ensemble of Clearing Differential Evolution (ECLDE) algorithm is proposed to handle multi-modal problems. In this algorithm, the population is evenly divided into 3 subpopulations and each of the subpopulations is assigned a set of niching parameters (clearing radius). The algorithms is tested on 12 benchmark multi-modal optimization problems and compared with the Clearing Differential Evolution (CLDE) with single clearing radius as well as a number of commonly used niching algorithms. As shown in the experimental results, the proposed algorithm is able to generate satisfactory performance over the benchmark functions.

Keywords

Differential evolution evolutionary computation multi-modal optimization niching 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Boyang Qu
    • 1
    • 3
  • Jing Liang
    • 2
  • Ponnuthurai Nagaratnam Suganthan
    • 3
  • Tiejun Chen
    • 2
  1. 1.School of Electric and Information EngineeringZhongyuan University of TechnologyChina
  2. 2.School of Electrical EngineeringZhengzhou UniversityChina
  3. 3.School of Electrical and Electronic EngineeringNanyang Technological UniversitySingapore

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