中文摘要：

针对基本灰狼优化算法（GWO）存在求解精度低、后期收敛速度慢和易陷入局部最优的问题,提出一种基于遗传算子的改进灰狼优化（IGWO）算法用于求解无约束优化问题.该算法首先利用佳点集理论初始化种群,为算法全局搜索多样性奠定基础;然后在决策层以外的群体中随机选取三个个体与决策层个体执行算术交叉操作,引导群体向决策层区域移动以增强算法局部搜索能力和加快算法收敛速度;最后,对决策层个体进行多样性变异操作以避免算法陷入局部最优.采用几个标准测试函数进行仿真实验：当维数较高（D=30或D=50）时,IGWO算法的总体性能上均优于基本GWO算法.实验结果表明IGWO算法在收敛速度和求解精度指标上明显优于对比算法.

英文摘要：

Aimed at the problem in standard grey wolf optimization （GWO） algorithm such as low solu- tion precision, slow later-term convergence, and high possibility of being trapped in local optimum, an improved GWO （IGWO） algorithm was proposed based on genetic operators for solving unconstrained optimization problems. In proposed IGWO algorithm, good point set theory was used to initiate population, which would enhance the diversity of global searching. Then three individuals were randomly selected from the population out of policy-making layer to lead the population move into the region of policy-making layer, so that the global searching ability and convergence were improved. Finally, the diversity mutation op- eration of individuals in policy-making layer was carried out to help them jump out from local optima. It was shown by simulation experiments on several benchmark functions that the proposed algorithm＇s overall performance would be superior to standard GWO algorithm when the dimension of functions was higher （D = 30 or D=50）. The convergence speed and solution precision with IGWO would remarkably be superior contrasted algorithms.