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Self-adaptive global mine blast algorithm for numerical optimization

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Abstract

In this article, a self-adaptive global mine blast algorithm (GMBA) is proposed for numerical optimization. This algorithm is designed in a novel way, and a new shrapnel equation is proposed for the exploitation phase of mine blast algorithm. A theoretical study is performed, which proves the convergence of any typical shrapnel piece; a new definition for parameters values is defined based on the performed theoretical studies. The promising nature of newly designed exploitation idea is verified with the help of multiple numerical experiments. A state-of-the-art set of benchmark problems are solved with the proposed GMBA, and the optimization results are compared with seven state-of-the-art optimization algorithms. The experimental results are statistically validated by using Wilcoxon signed-rank test, and time complexity of GMBA is also calculated. It has been justified that the proposed GMBA works as a global optimizer for constrained optimization problems. As an application to the newly developed GMBA, an important data clustering problem is solved on six data clusters and the clustering results are compared with the state-of-the-art optimization algorithms. The promising results claim the proposed GMBA as a strong optimizer for data clustering application.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) Grant Funded by the Korean government (MSIP) (No. 2013R1A2A1A01013886), National Institute of Technology Uttarakhand, India. We would like to express our gratitude toward the unknown potential reviewers who have agreed to review this article and who have provided valuable suggestions to improve the quality of the article.

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Authors and Affiliations

  1. Department of Mathematics, Dr. B. R. Ambedkar, National Institute of Technology, Jalandhar, 144-011, India

    Anupam Yadav

  2. Department of Mechanical Engineering, University Science and Culture, Tehran, Iran

    Ali Sadollah

  3. Department of Mathematics, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh, 177-005, India

    Neha Yadav

  4. School of Civil, Environmental and Architectural Engineering, Korea University, Seoul, 136-713, South Korea

    J. H. Kim

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  1. Anupam Yadav
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  2. Ali Sadollah
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  3. Neha Yadav
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  4. J. H. Kim
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Correspondence to Anupam Yadav.

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Yadav, A., Sadollah, A., Yadav, N. et al. Self-adaptive global mine blast algorithm for numerical optimization. Neural Comput & Applic 32, 2423–2444 (2020). https://doi.org/10.1007/s00521-019-04009-y

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