A new K-means grey wolf algorithm for engineering problems

Purpose This paper aims at studying meta-heuristic algorithms. One of the common meta-heuristic optimization algorithms is called grey wolf optimization (GWO). The key aim is to enhance the limitations of the wolves’ searching process of attacking gray wolves. Design/methodology/approach The develop...

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Bibliographic Details
Published in:World journal of engineering 2021-07, Vol.18 (4), p.630-638
Main Authors: Mohammed, Hardi M, Abdul, Zrar Kh, Rashid, Tarik A, Alsadoon, Abeer, Bacanin, Nebojsa
Format: Article
Language:English
Subjects:
Algorithms
Cluster analysis
Clustering
Data mining
Genetic algorithms
Heuristic
Heuristic methods
Optimization
Optimization algorithms
Performance evaluation
Researchers
Vector quantization
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Summary:Purpose This paper aims at studying meta-heuristic algorithms. One of the common meta-heuristic optimization algorithms is called grey wolf optimization (GWO). The key aim is to enhance the limitations of the wolves’ searching process of attacking gray wolves. Design/methodology/approach The development of meta-heuristic algorithms has increased by researchers to use them extensively in the field of business, science and engineering. In this paper, the K-means clustering algorithm is used to enhance the performance of the original GWO; the new algorithm is called K-means clustering gray wolf optimization (KMGWO). Findings Results illustrate the efficiency of KMGWO against to the GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve CEC2019 benchmark test functions. Originality/value Results prove that KMGWO is superior to GWO. KMGWO is also compared to cat swarm optimization (CSO), whale optimization algorithm-bat algorithm (WOA-BAT), WOA and GWO so KMGWO achieved the first rank in terms of performance. In addition, the KMGWO is used to solve a classical engineering problem and it is superior.
ISSN:1708-5284
2515-8082
DOI:10.1108/WJE-10-2020-0527