一种混合策略改进的灰狼优化算法

An Improved Grey Wolf Optimization Algorithm Based on Hybrid Strategy

为了改善传统灰狼优化算法初始种群不均匀、收敛速度慢和易陷入局部寻优的缺点,提出一种混合策略改进的灰狼优化算法。首先,基于反向学习策略生成多样化种群,为算法全局搜索奠定基础;然后,引入非线性调整策略平衡算法的全局和局部搜索能力,提升算法运行效率;最后,在位置更新中引入莱维飞行策略扩大搜索范围,增强算法全局搜索能力。通过12个标准基准函数测试算法性能。仿真结果表明,IGWO算法比传统GWO算法的寻优精度和稳定性平均提高了4倍。

In order to improve the disadvantages of traditional grey wolf optimizer(GWO),such as uneven initial population,slow convergence speed and easy to fall into local optimization,a hybrid strategy improved grey wolf optimization algorithm is proposed.Firstly,a variety of population is generated based on the reverse learning strategy,which lays the foundation for the global search of the algorithm.Secondly,the nonlinear adjustment strategy is introduced to balance the global and local search ability of the algorithm to improve the operation efficiency of the algorithm.Finally,Levy flight strategy is introduced into the position update to expand the search range and enhance the global search ability of the algorithm.The performance of the algorithm is tested by 12 benchmark functions.The simulation results show that the optimization accuracy and stability of IGWO algorithm are 4 times higher than that of traditional GWO algorithm.