Self-adaptive global mine blast algorithm for numerical optimization

Anupam Yadav, Ali Sadollah, Neha Yadav, Joong Hoon Kim

Research output: Contribution to journalArticle


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.

Original languageEnglish
JournalNeural Computing and Applications
Publication statusAccepted/In press - 2019 Jan 1


  • Constrained optimization
  • Data clustering
  • Global optimization
  • Mine blast algorithm

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence

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