# A New K means Grey Wolf Algorithm for Engineering Problems

arXiv: https://arxiv.org/abs/2103.05760 · HTML: https://mlsanity.com/paper/2103.05760/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 7 of its 80 references that are in the map.
- First posted on arXiv 27 Feb 2021.

## Builds on (most cited first, 7 of 7)

- [Fitness Dependent Optimizer: Inspired by the Bee Swarming Reproductive Process](https://mlsanity.com/paper/1904.05226/) (2019)
- [Cat Swarm Optimization Algorithm -- A Survey and Performance Evaluation](https://mlsanity.com/paper/2001.11822/) (2020)
- [Donkey and Smuggler Optimization Algorithm: A Collaborative Working Approach to Path Finding](https://mlsanity.com/paper/1904.09352/) (2019)
- [A Systematic and Meta-analysis Survey of Whale Optimization Algorithm](https://mlsanity.com/paper/1903.08763/) (2019)
- [A Multi Hidden Recurrent Neural Network with a Modified Grey Wolf Optimizer](https://mlsanity.com/paper/1903.11712/) (2019)
- [A Novel Hybrid GWO with WOA for Global Numerical Optimization and Solving Pressure Vessel Design](https://mlsanity.com/paper/2003.11894/) (2020)
- [A new evolutionary algorithm: Learner performance based behavior algorithm](https://mlsanity.com/paper/2009.11182/) (2020)

## Abstract (arXiv)

Purpose: The development of metaheuristic algorithms has increased by researchers to use them extensively in the field of business, science, and engineering. One of the common metaheuristic optimization algorithms is called Grey Wolf Optimization (GWO). The algorithm works based on imitation of the wolves' searching and the process of attacking grey wolves. The main purpose of this paper to overcome the GWO problem which is trapping into local optima. Design or Methodology or Approach: In this paper, the K-means clustering algorithm is used to enhance the performance of the original Grey Wolf Optimization by dividing the population into different parts. The proposed algorithm is called K-means clustering Grey Wolf Optimization (KMGWO). Findings: Results illustrate the efficiency of KMGWO is superior to GWO. To evaluate the performance of the KMGWO, KMGWO applied to solve 10 CEC2019 benchmark test functions. Results prove that KMGWO is better compared to GWO. KMGWO is also compared to Cat Swarm Optimization (CSO), Whale Optimization Algorithm-Bat Algorithm (WOA-BAT), and WOA, so, KMGWO achieves the first rank in terms of performance. Statistical results proved that KMGWO achieved a higher significant value compared to the compared algorithms. Also, the KMGWO is used to solve a pressure vessel design problem and it has outperformed results. 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. Also, the KMGWO is used to solve a classical engineering problem and it is superior