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A simple two-phase differential evolution for improved global numerical optimization

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Abstract

In the evolutionary computing community, differential evolution (DE) is well appreciated as a simple yet versatile population-based, non-convex optimizer designed for continuous optimization problems. A simple two-phase DE algorithm is presented in this article, which aims to identify promising basins of attraction on a non-convex functional landscape in the first phase, and starting from those previously identified search regions, a success history-based switch parameter DE is employed to further fine tune the search process leading to the optima of the landscape. Our proposed framework has been validated on the well-known IEEE Congress on Evolutionary Computation (CEC) benchmark suites (CEC 2013, 2014 and 2017). Results of the proposed method are compared with corresponding CEC winners (SHADE for CEC 2013, L-SHADE for CEC 2014 and jSO for CEC 2017).

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Authors and Affiliations

  1. Department of Computer Science and Technology, Indian Institute of Engineering Science and Technology, Shibpur, India

    Arka Ghosh & Asit Kr. Das

  2. ECSU, Indian Statistical Institute, Kolkata, India

    Swagatam Das

Authors
  1. Arka Ghosh
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  2. Swagatam Das
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  3. Asit Kr. Das
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Correspondence to Swagatam Das.

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Communicated by A. Di Nola.

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Ghosh, A., Das, S. & Das, A.K. A simple two-phase differential evolution for improved global numerical optimization. Soft Comput 24, 6151–6167 (2020). https://doi.org/10.1007/s00500-020-04750-w

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