Abstract
Grey Wolf Optimizer (GWO) is a new meta-heuristic search algorithm inspired by the social behavior of leadership and the hunting mechanism of grey wolves. GWO algorithm is prominent in terms of finding the optimal solution without getting trapped in premature convergence. In the original GWO, half of the iterations are dedicated to exploration and the other half are devoted to exploitation, overlooking the impact of right balance between these two to guarantee an accurate approximation of global optimum. To overcome this shortcoming, an Enhanced Grey Wolf Optimization (EGWO) algorithm with a better hunting mechanism is proposed, which focuses on proper balance between exploration and exploitation that leads to an optimal performance of the algorithm and hence promising candidate solutions are generated. To verify the performance of our proposed EGWO algorithm, it is benchmarked on twenty-five benchmark functions with diverse complexities. It is then employed on range based node localization problem in wireless sensor network to demonstrate its applicability. The simulation results indicate that the proposed algorithm is able to provide superior results in comparison with some well-known algorithms. The results of the node localization problem indicate the effectiveness of the proposed algorithm in solving real world problems with unknown search spaces.