The basic Grey Wolf Optimizer (GWO) algorithm is easy to fall into local optimum,which leads to low search precision.In order to solve the problem,an Improved GWO (IGWO) was proposed.On the one hand,the position vector updating equation was dynamically adjusted by introducing weighting factor derived from coefficient vector of the GWO algorithm.On the other hand,the probabilistic disturbance strategy was adopted to increase the population diversity of the algorithm at later stage of iteration,thus the ability of the algorithm for jumping out of the local optimum was enhanced.The simulation experiments were carried out on multiple benchmark test functions.The experimental results show that,compared with the GWO algorithm,Hybrid GWO (HGWO) algorithm,Gravitational Search Agorithm (GSA) and Differential Evolution (DE) algorithm,the proposed IGWO can effectively get rid of local convergence and has obvious advantages in search precision,algorithm stability and convergence speed.%针对基本灰狼优化(GWO)算法存在易陷入局部最优,进而导致搜索精度偏低的问题,提出了一种改进的GWO (IGWO)算法.一方面,通过引入由GWO算法系数向量构成的权值因子,动态调整算法的位置向量更新方程;另一方面,通过采用概率扰动策略,增强算法迭代后期的种群多样性,从而提升算法跳出局部最优的能力.对多个基准测试函数进行仿真实验,实验结果表明,相对于GWO算法、混合GWO(HGWO)算法、引力搜索算法(GSA)和差分进化(DE)算法,所提IGWO算法有效摆脱了局部收敛,在搜索精度、算法稳定性以及收敛速度上具有明显优势.
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