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

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Intelligent Systems Design and Applications (ISDA 2018 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((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.

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Authors and Affiliations

  1. Graduate Program in Applied Computing, Santa Catarina State University, Joinville, SC, Brazil

    Gabriel Dominico, Mateus Boiani & Rafael Stubs Parpinelli

Authors
  1. Gabriel Dominico
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  2. Mateus Boiani
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  3. Rafael Stubs Parpinelli
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Corresponding author

Correspondence to Rafael Stubs Parpinelli .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs, Auburn, WA, USA

    Ajith Abraham

  2. School of Information Technology and Engineering, Vellore Institute of Technology, Vellore, Tamil Nadu, India

    Aswani Kumar Cherukuri

  3. Tijuana Institute of Technology, Tijuana, Mexico

    Patricia Melin

  4. Machine Intelligence Research Labs, Auburn, WA, USA

    Niketa Gandhi

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Dominico, G., Boiani, M., Parpinelli, R.S. (2020). A Self-adaptive Differential Evolution with Local Search Applied to Multimodal Optimization. In: Abraham, A., Cherukuri, A.K., Melin, P., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2018 2018. Advances in Intelligent Systems and Computing, vol 940. Springer, Cham. https://doi.org/10.1007/978-3-030-16657-1_107

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