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.
| Original language | English |
|---|---|
| Journal | Neural Computing and Applications |
| DOIs | |
| Publication status | Accepted/In press - 2019 Jan 1 |
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Keywords
- Constrained optimization
- Data clustering
- Global optimization
- Mine blast algorithm
ASJC Scopus subject areas
- Software
- Artificial Intelligence
Cite this
Self-adaptive global mine blast algorithm for numerical optimization. / Yadav, Anupam; Sadollah, Ali; Yadav, Neha; Kim, Joong Hoon.
In: Neural Computing and Applications, 01.01.2019.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Self-adaptive global mine blast algorithm for numerical optimization
AU - Yadav, Anupam
AU - Sadollah, Ali
AU - Yadav, Neha
AU - Kim, Joong Hoon
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Constrained optimization
KW - Data clustering
KW - Global optimization
KW - Mine blast algorithm
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U2 - 10.1007/s00521-019-04009-y
DO - 10.1007/s00521-019-04009-y
M3 - Article
AN - SCOPUS:85060171295
JO - Neural Computing and Applications
JF - Neural Computing and Applications
SN - 0941-0643
ER -