Abstract: Aiming at the disadvantages of grey wolf optimization（GWO）, such as insufficient global search ability and easy to fall into local optimization in complex optimization problems, an improved grey wolf optimization（LGWO） with Levy flight and dynamic weight strategy is proposed. Firstly, Singer mapping is used to initialization to increase the diversity of the population. Secondly, a new nonlinear convergence factor updating strategy is adopted to balance the global and local search abilities. Finally, Levy flight and dynamic weight strategy are introduced into the position update formula to reduce the risk of falling into local optimum and improve the optimization accuracy. The performance of algorithm is evaluated by comparing experiments on 8 benchmark functions with other optimization algorithms and improved algorithms. The experimental results indicate that the LGWO algorithm is superior to other algorithms in convergence speed and prediction accuracy and the validity of the LGWO algorithm in high dimensional problems is verified.

Key words: grey wolf optimization algorithm, Levy flight, dynamic weight, Singer mapping

摘要： 针对求解复杂优化问题时，灰狼（GWO）算法存在全局搜索能力不足、容易陷入局部最优值等问题，提出一种引入莱维飞行与动态权重策略的改进灰狼算法（LGWO）。基于Singer混沌映射初始化灰狼个体位置，增加种群多样性；收敛因子采用新的非线性更新策略，在种群迭代全期平衡全局搜索与局部搜索能力；在种群位置更新公式引入莱维飞行与动态权重策略，增加种群跳出局部最优值的概率，提升寻优准确度。通过8个基准函数的测试，并与其他优化算法和改进算法进行对比，LGWO取得了最优的收敛速度与预测精度，并验证了LGWO算法优化高维复杂问题的有效性。

关键词: 灰狼优化算法, 莱维飞行, 动态权重, Singer映射