Grey Wolf Optimization Algorithm with Multiple Strategy Improvements
When solving complex optimization problems,the grey wolf optimization algorithm has the disadvantages of slow convergence speed and easy falling into local extremes.To address these issues,a grey wolf optimization algorithm that integrated multiple strategy improvements was proposed.Firstly,a chaotic sequence was used to generate an initial population that was uniformly distributed in the solu-tion space.Then,combined with the elite reverse learning mechanism,the optimal solution was searched,and a convergence stagnation monitoring strategy was introduced to improve the overall anti-stagnation ability of the algorithm and maintain population diversity.Finally,a non-linear dynamic ad-justment strategy for convergence factors was proposed to improve the global convergence speed and sta-bility of the algorithm,and simulation experiments were conducted on 10 classic high-dimensional test functions.The experimental results show that the improved algorithm can effectively eliminate local ex-treme points,and its global optimization performance is better than the standard grey wolf optimization algorithm.
grey wolf optimization algorithmsimplex methodoptimizationconvergence factor
万方数据
维普
