Enhancement of gray wolf optimizer using differential evolution mechanism

In order to solve the low accuracy and local optimization problems of standard grey wolf optimizer, an improved grey wolf optimizer (DE-GWO) based on differential evolution mechanism is proposed. The algorithm initializes the population by chaotic mapping iterative to increase the diversity of the population and uses a differential evolution mechanism to cross, mutate and select the gray wolf population to accelerate the convergence speed and improve the convergence precision. The simulation experiments of eight test functions are carried out to highlight the proposed algorithm’s characteristics. The numerical results and related analysis show that the improved gray wolf optimization algorithm has higher precision and better stability than other GWO variants.