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Self-adaptive global mine blast algorithm for numerical optimization

Authors
Yadav, AnupamSadollah, AliYadav, NehaKim, J. H.
Issue Date
4월-2020
Publisher
SPRINGER LONDON LTD
Keywords
Mine blast algorithm; Global optimization; Constrained optimization; Data clustering
Citation
NEURAL COMPUTING & APPLICATIONS, v.32, no.7, pp.2423 - 2444
Indexed
SCIE
SCOPUS
Journal Title
NEURAL COMPUTING & APPLICATIONS
Volume
32
Number
7
Start Page
2423
End Page
2444
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/56806
DOI
10.1007/s00521-019-04009-y
ISSN
0941-0643
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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