A novel enhanced global exploration whale optimization algorithm based on Lévy flights and judgment mechanism for global continuous optimization problems

Whale optimization algorithm (WOA) is a very popular meta-heuristic algorithm. When optimizing complex multi-dimensional problems, the WOA has problems such as poor convergence behavior and low exploration efficiency. To improve the convergence behavior of the WOA and strengthen its global exploration efficiency, we propose a novel enhanced global exploration whale optimization algorithm (EGE-WOA). First, Lévy flights have the ability to strengthen global space search. For unconstrained optimization problems and constrained optimization problems, the EGE-WOA introduces Lévy flights to enhance its global exploration efficiency. Then, the EGE-WOA improves its convergence behavior by introducing new convergent dual adaptive weights. Finally, according to the characteristics of sperm whales hunting by emitting high-frequency ultrasound, the EGE-WOA introduces a new mechanism for judging the predation status of whales. The judgment mechanism is to judge the three predation states of whales by judging the fitness value between the optimal whale individual and any whale individual. The proposed new judgment mechanism can indeed effectively improve the global exploration efficiency of the WOA. For the exploration efficiency of the unconstrained optimization problems and constrained optimization problems, the EGE-WOA combines the Lévy flights and judgment mechanism in different ways to achieve efficient exploration efficiency and better convergence behavior. The experimental results show that in the optimization process of 33 unconstrained benchmark functions and 6 constrained real cases, the mean and standard deviation of the EGE-WOA are better than other algorithms.