Enhanced grey wolf optimizer with a model for dynamically estimating the location of the prey

Abstract

Abstract Grey Wolf Optimizer (GWO) is a new meta-heuristic inspired by the hunting behavior of grey wolves. Our findings reveal that the optimizer has a strong search bias towards the origin of the coordinate system. In this article, a more realistic model is proposed to mimic the leadership hierarchy and group hunting mechanism of grey wolves in nature. In the innovative model, the location of the prey is dynamically estimated by leader wolves and each wolf is directly moving towards the estimated location of the prey. The proposed algorithm is compared with the original grey wolf optimizer and its recent variants on the CEC2017 test suite. The experimental results indicate that the enhanced optimizer significantly outperforms the original version and recent variants in terms of the convergence speed and the quality of solution found. The proposed algorithm also achieves the best solutions in solving two real engineering optimization problems at a lower computation cost.