Research on Improving Gray Wolf Algorithm Based on Multi-Strategy Fusion

To address the shortcomings of basic Gray Wolf Optimization (GWO) algorithm in solving complex problems, such as relying on initial population, converging too early, and easily falling into local optimality, a chaotic reverse learning initialization strategy, nonlinear control parameter convergence dynamic position update strategy are introduced to develop multi-strategy fusion Improved (IGWO) algorithm, this method is used solve function optimization problems. First, backward based logistic mapping learning, adopted improve random GWO enhance traversal diversity population. Second, for perturbation constructed avoid problem premature due linear balance exploration exploitation ability algorithm. Finally, location guidance weights individual memory proposed effectively algorithm’s accuracy computational efficiency; meanwhile, Gaussian-Cauchy mutation superior selection optimize optimal α wolves population jump out extremes. Simulation experiments conducted 11 classical test functions, results show that improved IGWO gray 10 other standard swarm intelligence algorithms 4 terms solution accuracy, speed, stability. It provides new