求解带硬时间窗的多目标车辆路径问题的多种混合蝙蝠算法

Multi-hybrid bat algorithm for solving multi-objectives vehicle routing problem with hard time-window

针对多目标车辆路径问题的研究,考虑了车载量限制和硬时间窗的约束条件,以最小派车数和最小车辆行驶距离为目标建立了数学模型。在分析基本蝙蝠算法求解离散问题局限性的基础上,混合蝙蝠法加入交叉算子和重组算子,提高算法性能。利用遗传算法的特点,构建出三种混合蝙蝠算法。算例测试结果表明,混合蝙蝠算法是解决离散型问题的一种有效方法。与基本蝙蝠算法相比,混合蝙蝠算法具有较高的计算效率和持续优化能力,其中单点重组精英遗传混合蝙蝠算法解决算例寻优能力最佳。

Based on the multi-objective vehicle-routing-problem,this paper considered the vehicle restrictions and hard timewindow and established a mathematical model with the objectives for the shortest length of vehicle travel and the minimum number of the using vehicles. On the basis of the analysis for the basic bat algorithm's limitation in solving the discrete problem,hybrid bat algorithms added the crossover operator and recombination operator to prove the algorithm's capacility. This paper proposed 3 hybrid bat algorithms which took advantages of the genetic algorithm. The simulation results for the test case show that the hybrid bat algorithm is an effective algorithm in solving the discrete problem. Also,the hybrid bat algorithm has the advantages of better computational efficiency and continuous optimization ability compared to the basic bat algorithm. The monogene recombination genetic has a remarkable optimization ability in sloving instance among others.