An improved grey wolf optimization algorithm based on bounded subpopulation re-search strategy

Abstract Swarm intelligence optimization algorithms are widely used in solving and optimizing complex problems in various industries due to their simple and effective structure, as well as ease of deployment and implementation. The paper puts forward a new grey wolf optimization (GWO) algorithm by using bounded grey wolf subpopulation re-search strategy to deal with problems of local optima and low precision in solving complex control, decision-making, and planning problems in intelligent optimization algorithms. Firstly, based on the traditional GWO algorithm model, the intelligent individuals, leadership selection, and location update methods of the basic algorithm are analyzed, and the basic process of grey wolf optimization is established. Secondly, by adopting three strategies including boundary reflection, secondary search, and particle swarm optimization update, a new update model and search mechanism are designed for the GWO algorithm, effectively solving the problem of the gray wolf optimization algorithm being prone to falling into local optima and premature convergence. Finally, the effectiveness and superiority of the proposed algorithm are tested through numerical simulation and comparative experiments.