COMBINING GREY WOLF OPTIMIZATION AND WHALE OPTIMIZATION ALGORITHM FOR BENCHMARK TEST FUNCTIONS

Many optimization problems have been successfully addressed using metaheuristic approaches. These approaches are frequently able to choose the best answer fast and effectively. Recently, use of swarm-based algorithms, a kind approach, has become more common. In this study, hybrid method called WOAGWO is proposed by combining Whale Optimization Algorithm (WOA) Grey Wolf (GWO). This aims realize effective algorithm positive aspects two algorithms. 23 benchmark test functions were utilized assess WOAGWO. By running approach 30 times, mean fitness standard deviation values computed. results compared WOA, GWO, Ant Lion (ALO), Particle Swarm (PSO), Improved ALO (IALO) in literature. The algorithm, when these algorithms literature, produced optimal 5 7 unimodal functions, 4 6 multimodal 9 10 fixed-dimension functions. Therefore, suggested generally outperforms findings seems be promising it wide range uses.