A new K-means grey wolf algorithm for engineering problems

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