带时间窗的车辆路径问题:一种混合粒子群优化方法

Xiaoxiang Liu, Weigang Jiang, Jianwen Xie
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引用次数: 13

摘要

车辆路径问题(VRP)是一个众所周知的组合优化和非线性规划问题,它寻求用一个车队为多个客户提供服务。针对VRP问题,提出了一种混合粒子群算法。该算法利用了遗传算法(GA)中原有的交叉操作,使其更易于操作,避免陷入局部最优,同时为了提高算法的收敛速度,还加入了水平集理论。我们将粒子群算法应用于VRP实例,并将其结果与粒子群算法、遗传算法和并行粒子群算法的结果进行了比较。实验结果表明,该算法的性能优于其他算法,将成为求解离散组合问题的有效方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Vehicle Routing Problem with Time Windows: A Hybrid Particle Swarm Optimization Approach
Vehicle routing problem (VRP) is a well-known combinatorial optimization and nonlinear programming problem seeking to service a number of customers with a fleet of vehicles. This paper proposes a hybrid particle swarm optimization (HPSO) algorithm for VRP. The proposed algorithm utilizes the crossover operation that originally appears in genetic algorithm (GA) to make its manipulation more readily and avoid being trapped in local optimum, and simultaneously for improving the convergence speed of the algorithm, level set theory is also added to it. We employ the HPSO algorithm to an example of VRP, and compare its result with those generated by PSO, GA, and parallel PSO algorithms. The experimental comparison results indicate that the performance of HPSO algorithm is superior to others, and it will become an effective approach for solving discrete combinatory problems.
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