A Comparison between Grey Wolf Global Optimization Algorithm and two other Global Optimization Algorithms
Abstract
In this paper a comprehension comparison between the Grey Wolf Global Optimization Algorithm (GWA) as a newly presented global optimization algorithm with two other wall-known algorithms including Cuckoo Search optimization algorithm (CSA), and Bat Optimization Algorithm (BA). All algorithms are applied on four complex benchmark functions. The purpose of this work is to identify the best algorithm in terms of converge speed and efficiency in finding the global optimum solution, where the converge speed is measured in terms the number of function evaluations. The simulation results show that the GWA algorithm with less function evaluations becomes first if the simulation time is important, while if efficiency is the significant issue, BA and CSA would have a better performance.
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