The Development and Application Research of Grey Wolf Optimization Algorithm

Abstract

The Grey Wolf Optimization (GWO) algorithm is an emerging swarm intelligence optimization technique known for its simplicity, minimal control parameters, fast convergence, and ease of implementation. This paper investigates the search mechanism and implementation process of the GWO algorithm, analyzing its shortcomings, including poor population diversity, slow convergence in later stages, and susceptibility to local optima. It provides an overview of improvement strategies for the Grey Wolf Optimization algorithm, encompassing enhancements to the population, parameters, search mechanism, and its integration with other optimization algorithms. Furthermore, the paper discusses the applications of the Grey Wolf Optimization algorithm in various domains.