Meta-Heuristic Algorithm Inspired by Grey Wolves for Solving Function Optimization Problems

In this paper, we suggest the use of Grey Wolf Optimization (GWO) algorithm to solve numerical optimization problems. GWO is compared with two well-known optimization algorithms namely, Bat Algorithm (BA) and Particle Swarm Optimization (PSO), to test the improvement in the accuracy of finding the near optimal solution and the reduction in the computational cost. Ten standard benchmark functions were applied to test the performance of the three optimization algorithms in terms of accuracy and computational cost. The experimental results proved that our proposed method achieved accuracy better than the other two algorithms and it reduced the computational cost and converged rapidly to the optimal solution.