关键词: 元启发式算法, 灰狼算法, 函数优化, 权值因子, 扰动策略

Abstract: The basic grey wolf optimizer tends to stuck in local optimum, leading to low search precision. In order to handle this issue, an improved grey wolf optimizer is proposed. The position vector updating equation is dynamically adjusted by introducing weighting factor derived from coefficient vector in the traditional method. The probabilistic disturbance strategy is adopted to increase the population diversity at the later stage of the algorithm, enhancing the ability of the algorithm to jump out of the local optimum. The simulation results of many benchmark functions show that compared with other optimization algorithms, the improved grey wolf optimizer can effectively get rid of the local convergence and has obvious advantages in search precision, algorithm stability and convergence speed.

Key words: meta-heuristic algorithm, grey wolf optimizer, function optimization, weighting factor, disturbance strategy