Affiliation:
1. Department of Computer Science and Artificial Intelligence College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou 325035 China
2. School of Computing and Mathematical Sciences University of Leicester Leicester LE1 7RH UK
3. Department of Biological Sciences Xi'an Jiaotong‐Liverpool University Suzhou Jiangsu 215123 China
4. Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Jeddah 21589 Saudi Arabia
Abstract
Grey wolf optimizer (GWO) is a highly valued heuristic algorithm in many fields. However, for some complex problems, especially high‐dimensional and multimodal problems, the basic algorithm has limited computational power and cannot get a satisfactory answer. In order to find a better solution, an improved algorithm based on GWO is proposed herein. Gaussian barebone, random selection and chaotic game mechanisms are introduced into the GWO algorithm to enhance the global search ability. The GWO enhanced by three mechanisms is called CBRGWO. To verify the performance of CBRGWO, using IEEE CEC 2017 as a test function, CBRGWO is compared to five GWO variants, five basic algorithms, six advanced algorithms, and four champion algorithms. CBRGWO is evaluated using the Friedman test and Wilcoxon signed‐rank test. Then, the stability of CBRGWO is analyzed. To verify that CBRGWO is still effective in practical application, CBRGWO is applied to five engineering problems and a water quality prediction problem. The experimental findings indicate that CBRGWO maintains excellent optimization ability in practical engineering problems.