重选精英个体的非线性收敛灰狼优化算法

针对灰狼优化算法（GWO）存在的求解精度较低、后期收敛速度较慢、易陷入局部最优的缺点，提出一种改进灰狼优化算法（EGWO）。该算法引进两种改进策略：用以平衡算法全局搜索性和局部开发性的非线性收敛因子调整策略和用以降低陷入局部最优风险的精英个体重选策略。通过在9个基准测试函数上的实验与标准GWO算法，以及文献提出的5种改进灰狼算法和4种其他算法进行对比，从算法寻优的精确性和鲁棒性两个方面验证两种算法改进策略的有效性。实验结果表明，两种改进策略都能提升算法性能，综合使用两种策略的EGWO在收敛速度和求解精度都明显优于其他比较算法。

Improved Grey Wolf Optimizer Algorithm Using Nonlinear Convergence Factor and Elite Re-election Strategy

Aiming at the disadvantages of Grey Wolf Optimization(GWO),such as low solution accuracy,slow convergence speed and easy to fall into local optimization,an improved Grey Wolf Optimization(EGWO)is proposed.Two improvement strategies are introduced into the algorithm:the adjustment strategy of nonlinear convergence factor to balance global search and local development of the algorithm,and the elite re-election strategy to reduce the risk of falling into the local optimal.Through comparing experiments on 9 benchmark test functions with standard GWO algorithm,5 improved GWO proposed by other literatures and 4 other algorithms,from algorithm optimization ability and robustness two aspects to demonstrate the effectiveness of two kinds of algorithm improvement strategies,the experimental results show that both strategies can improve algorithm performance,EGWO that comprehensive use of two strategies is superior to other comparison algorithms in convergence speed and solution accuracy.