IMPROVEMENT OF WOLF LEADER IN THE GREY WOLF OPTIMIZATION

Authors : Onur İNAN, Mustafa Serter UZER

Pages : 557-570

Doi:10.36306/konjes.1209089

View : 50 | Download : 53

Publication Date : 2023-06-01

Article Type : Research Paper

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 insert ignore into journalissuearticles values(GWO);, has been improved with the Whale Optimization Algorithm insert ignore into journalissuearticles values(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.
Keywords : Gri Kurt Optimizasyonu, Alfa Kurt, Balina Optimizasyon Algoritması, Benchmark Test Fonksiyonları, Grey Wolf Optimization, Alpha Wolf, Whale Optimization Algorithm, Benchmark Test Functions