混合蝙蝠算法在包装件配送中的应用研究

目的使蝙蝠算法(BA)适应包装件配送车辆路径问题(VRP)的求解,并提高该算法的求解性能。方法在标准BA算法的基础上提出混合蝙蝠算法(HBA)。首先,设计改进的蝙蝠算法(IBA),使其能够适用于包装件配送VRP问题的求解。其次,引入混沌系统,对IBA算法进行混沌初始化。然后,设计裂变算子和变异算子。在IBA算法迭代前半段,将蝙蝠种群中较差的一半蝙蝠重新混沌初始化,以提高种群多样性。在IBA算法迭代后半段,对陷入局部最优解的蝙蝠进行鲶鱼扰动。最后,提出HBA算法并对企业实例进行仿真测试。结果 HBA算法求得的最优配送距离为773.01 km,相对于GA算法(781.25 km)和IBA算法(786.04 km)分别节约了8.24 km和13.03 km。结论与IBA算法和GA算法相比,HBA算法求解包装件配送VRP问题的全局优化能力更强、收敛速度更快。

Application of Hybrid Bat Algorithm in Package Distribution

The work aims to enable the bat algorithm (BA) to be applied to solve the vehicle routing problem (VRP) in package distribution, and improve the solving performance of BA. Based on the standard BA, a hybrid BA (HBA) was proposed. Firstly, the improved BA (IBA) was designed to solve VRP in package distribution. Secondly, the chaotic system was introduced to initialize IBA. Then, the fission operator and mutation operator were designed. In order to improve the population diversity, the worse half bats in the group were reinitialized by chaotic system during the first half iteration. In the second half iteration, the catfish disturbance was carried out on the bats fallen into the local optimal solution. Finally, the HBA was put forward and simulation tests were carried out on an enterprise instance. The optimal distribution distance obtained by HBA was 773.01 km, which respectively saved 8.24 km and 13.03 km compared with the GA (781.25 km) and BA (786.04 km). Compared with IBA and GA, the HBA has stronger global optimization ability and faster convergence speed when used to solve the VRP in package distribution.