关键词: 改进灰狼优化算法, 个体历史最优位置, 早熟判别指标, 反向搜索因子, beta随机调整数

Abstract:

Aiming at the disadvantages of Grey Wolf Optimization（GWO）, such as easy to fall into local optimization and slow convergence speed, an improved GWO algorithm based on dynamic reverse search for updated position（DAGWO） is proposed. In order to accelerate the convergence speed of the algorithm, the optimal position guidance strategy of individual history is introduced into the original position updating formula. At the same time, a reverse search factor is introduced, which dynamically adjusts its value according to the early-maturity discrimination index of the population. When the algorithm falls into the local optimum, gray wolf individuals are reversely searched to the direction of the worst individual in the whole population, so as to improve the ability of the population to jump out of the local optimum. In addition, a novel nonlinear convergence factor for local perturbation is constructed to balance the global and local search capabilities of the algorithm. The simulation results of 20 classical test functions show that the DAGWO algorithm is superior to the standard intelligent optimization algorithm and other related improved algorithms in solving precision, convergence speed and stability of the algorithm.

Key words: improved grey wolf optimization algorithm, the best position in individual history, early maturity index, reverse search factor, beta random adjustment