Research on Grey Wolf Optimization Algorithm Based on Adaptive Adjustment Strategy

A grey wolf optimization algorithm based on an adaptive adjustment strategy (Improvements-Grey Wolf Optimization, IGWO) is proposed in this paper to address the issues with the Grey Wolf Optimization (GWO) algorithm, which has a slow convergence speed in the later stages and where the local search and global search cannot be taken into effective balance. First, a chaotic logistic map is used to initialize the population. Next, an inverse trigonometric function-based mathematical model is developed to achieve convergence purposes. Finally, a new location update method is used to update the next-generation location to improve search traversability. Additionally, the CEC2013-Benchmark standard test function was used to evaluate the benefits of performance compared to the standard GWO, PSO, and DEGWO (DE-Grey wolf optimization) algorithms. The improved GWO algorithm has greatly increased the convergence speed, search capability, and stability. It performs better than the other three algorithms as well.