IMPROVEMENT OF WOLF LEADER IN THE GREY WOLF OPTIMIZATION

Abstract

The development of optimization algorithms attracts the attention of many analysts as it has advantages such as increasing performance, revenue, and efficiency in various fields, and reducing cost. Swarm-based optimization algorithms, which are among the meta-heuristic methods, are more commonly preferred because they are generally successful. In this study, the alpha wolf class, also called the wolf leader class, in the Grey Wolf Optimization (GWO), has been improved with the Whale Optimization Algorithm (WOA). This improved method is called ILGWO. To evaluate the ILGWO, 23 benchmark test functions, and 10 CEC2019 test functions were used. After running 30 iterations of the suggested algorithm, average fitness and standard deviation values have been acquired; these findings have been compared to the literature. Based on the literature's comparisons of the algorithms, the ILGWO algorithm has achieved the most optimal result in 5 of 7 functions for unimodal benchmark functions, 3 of 6 functions for multimodal benchmark functions, 9 of 10 functions for fixed-dimension multimodal benchmark functions, and 8 of 10 functions for CEC2019 test functions. So the proposed algorithm is generally better than the literature results. It has been found that the suggested ILGWO is encouraging and may be used in a variety of implementations.