求解铁路物流配送中心选址问题的改进灰狼优化算法

针对单一机制的灰狼优化算法（GWO）易陷于局部最优、收敛速度慢的问题，提出了一种改进灰狼优化（IGWO）算法来解决实际铁路物流配送中心选址的问题。首先，在基本的灰狼优化算法的基础上，引入佳点集理论初始化种群，从而提高了初始种群的多样性；然后，利用差值剔除策略（DES）来增加全局寻优能力，以达到一种高效的寻优模式。仿真实验结果表明：与标准的灰狼算法相比，所提出的IGWO适应度值提高了3%，在10个测试函数中最优值精度可最多提高7个单位；与粒子群优化（PSO）算法、差分进化（DE）算法和遗传算法（GA）比较，所提算法的运行速度分别提高了39.6%、46.5%和65.9%，选址速度也明显提高。可见所提算法可用于铁路物流中心的选址。

Improved grey wolf optimizer for location selection problem of railway logistics distribution center

The single mechanism based Grey Wolf Optimizer (GWO) is easy to fall into local optimum and has slow convergence speed. In order to solve the problems, an Improved Grey Wolf Optimization (IGWO) was proposed to solve the actual location selection problem of railway logistics distribution center. Firstly, based on the basic GWO, the theory of good point set was introduced to initialize the population, which improved the diversity of the initial population. Then, the D-value Elimination Strategy (DES) was used to increase the global optimization ability, so as to achieve an efficient optimization mode. The simulation results show that, compared with the standard GWO, IGWO has the fitness value increased by 3%, and the accuracy of the optimal value increased by up to 7 units in 10 test functions. Compared with Particle Swarm Optimization (PSO) algorithm, Differential Evolution (DE) algorithm and Genetic Algorithm (GA), IGWO has the location selection speed increased by 39.6%, 46.5% and 65.9% respectively, and the location selection velocity is significantly improved. The proposed algorithm can be used for railway logistics center location selection.