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A Self-adaptive Differential Evolution with Local Search Applied to Multimodal Optimization

  • Gabriel Dominico
  • Mateus Boiani
  • Rafael Stubs ParpinelliEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 940)

Abstract

The main difficulty encountered by population-based approaches in multimodal problems is their loss of diversity while converging to an optimum. Also, it is known that parameters play a big role in the performance of metaheuristics. Hence, in this paper two variations of the NCDE algorithm for multimodal optimization are proposed. The first version applies the jDE self-adaptive mechanism for parameter tuning along with the neighborhood mutation and crowding strategies, called NCjDE. The second version adds to the first the Hooke-Jeeves direct search at the end of the optimization process, called NCjDE-HJ. The proposed algorithm is compared in terms of peak ratio with three other state-of-the-art algorithms and results obtained show that the proposed variations are competitive for multimodal problems.

Keywords

Multimodal optimization Differential Evolution Parameter control Niching Local search Hooke-Jeeves 

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Gabriel Dominico
    • 1
  • Mateus Boiani
    • 1
  • Rafael Stubs Parpinelli
    • 1
    Email author
  1. 1.Graduate Program in Applied ComputingSanta Catarina State UniversityJoinvilleBrazil

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