改进智能水滴算法求解混合时间窗车辆路径问题

Improved intelligent water droplet algorithms for solving vehicle routing problem with mixed time windows

摘要:

针对多目标车辆路径问题,研究了车载量、配送里程、混合时间窗等限制约束条件下,以最小配送费用和最少配送车辆数为目标建立多目标数学模型。在分析智能水滴算法求解类似离散问题时存在的局限性基础上,运用多种方式对其进行改进,并引入遗传算法选择、交叉及重组算子提高其性能,构建出两种改进智能水滴遗传混合算法,运用Solomon标准测试算例和实际算例进行验证。比较结果显示,改进后的混合算法能够有效解决离散问题,在持续寻优能力上较传统智能水滴算法和遗传算法更优；并且竞争选择改进智能水滴遗传混合算法求解算例效果最优。

Abstract:

For multi-objective vehicle routing problem, considering the constraints of vehicle volume, delivery mileage and mixed time windows, a multi-objective mathematical model is established to minimize the cost of distribution and the minimum number of vehicles. In the presence of intelligent analysis algorithm for solving discrete problems similar to drop the limitation on the basis of using a variety of ways to improve the intelligent algorithm and genetic algorithm to drop, selection, crossover and recombination operators, constructed two kinds of improved intelligent water mixed genetic algorithm, improves the algorithm performance. A practical example is designed and compared. The results of the example test show that the improved intelligent water droplet genetic hybrid algorithm is an effective method to solve the discrete problem. Compared with the basic intelligent water drop algorithm, the improved intelligent droplet genetic hybrid algorithm has higher computing efficiency and continuous optimization capability, and the competition selection improves the intelligent droplet genetic hybrid algorithm is the best solution of the example.