基于个体记忆和高斯扰动的自适应灰狼算法OA

Aiming at the problems of local optimization and slow convergence speed of traditional gray wolf algorithms in solving complex optimization problems,an adaptive gray wolf optimization algorithm(NGWO)based on individual memory and Gaussian perturbation has been proposed.Firstly,a nonlinear control parameter is introduced to balance the global exploration and local development abilities of the algorithm.Secondly,an adaptive position update formula with individual memory is proposed,which is used to accelerate the convergence speed and accuracy of the algorithm.Then,the ability of the algorithm to jump out of local optimum is strengthened by combining greedy algorithm and adaptive Gaussian perturbation.Through the test of the benchmark function,NGWO has a more accurate solution and a higher convergence speed compared with other optimization algorithms and improved algorithms.