一种改进的灰狼算法

An Improved Grey Wolf Algorithm

针对灰狼算法的缺点,提出一种改进的灰狼算法。引入混沌策略初始化种群,产生更均匀的初始种群;将线性收敛因子改进为非线性收敛因子,能更好地均衡全局搜索和局部搜索;在灰狼更新位置时,根据头狼的决策能力,加入权重策略,使每个灰狼个体更快地向最优的位置移动。最后用6个标准测试函数做仿真实验,结果表明,改进后的灰狼算法在单峰函数和多峰函数求解中,收敛速度和寻优精度都优于其他算法。

Aiming at the shortcomings of Gray Wolf algorithm,an improved Gray Wolf algorithm is proposed,and chaos strategy is introduced to initialize the population to produce a more uniform initial population.The linear convergence factor is improved to the nonlinear convergence factor,which can better balance the global search and local search.When the gray wolf updates the position,according to the decision-making ability of the head wolf,a weight strategy is added to make each gray wolf move to the optimal position faster.Finally.Six standard test functions are used to do simulation experiments.The results show that the improved Gray Wolf algorithm has better convergence speed and optimization accuracy than other algorithms in solving single peak function and multi-peak function.