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A new hybrid differential evolution algorithm with self-adaptation for function optimization

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Abstract

In this study, a new hybrid algorithm, hDEBSA, is proposed with the aid of two evolutionary algorithms, Differential Evolution (DE) and Backtracking Search Optimization Algorithm (BSA). The control parameters of both algorithms are simultaneously considered as a self-adaptation basis such that the values of the parameters update automatically during the optimization process to improve performance and convergence speed. To validate the proposed algorithm, twenty-eight CEC2013 test functions are considered. The performance results of hDEBSA are validated by comparing them with several state-of-the-art algorithms that are available in literature. Finally, hDEBSA is applied to solve four real-world optimization problems, and the results are compared with the other algorithms, where it was found that the hDEBSA performance is better than that of the other algorithms.

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Acknowledgments

The authors are extremely thankful to anonymous referees and the editor for their valuable comments towards improving the manuscript.

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Authors and Affiliations

  1. Department of Mathematics, National Institute of Technology Agartala, Barjala, Jirania, 799046, West Tripura, India

    Sukanta Nama & Apu Kumar Saha

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  1. Sukanta Nama
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  2. Apu Kumar Saha
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Correspondence to Apu Kumar Saha.

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Nama, S., Saha, A.K. A new hybrid differential evolution algorithm with self-adaptation for function optimization. Appl Intell 48, 1657–1671 (2018). https://doi.org/10.1007/s10489-017-1016-y

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