基于PSO-ACO融合算法的物流车辆路径优化与控制研究

A novel PSO-ACO fusion algorithm for logistics distribution vehicle routing optimization

传统蚁群算法在解决物流配送路径问题时容易出现“早熟”问题,使路径寻找速度和优化结果受到影响。为更合理进行车辆路径调度管理,提出一种粒子群-蚁群相融合的物流配送路径规划算法,该算法充分利用粒子群较强的全局搜索能力和搜索速度快的特点,将得到的次优解转化为蚁群算法中的初始信息素的增量,最后利用蚁群算法的正反馈机制求解问题的精确解。研究结果表明:与单一算法相比,融合算法能快速有效地确定物流配送路径,具有较快的寻优速度和收敛精度,更合理的控制物流配送成本。

Traditional ant colony optimization(ACO)algorithm may suffer from‘premature’when planning the routing of logistics distribution,which results in a low speed of routing scheduling and optimization.In this paper,a novel logistics distribution routing planning algorithm is proposed by a subsequent combination of particle swarm optimization(PSO)and ant colony optimization.The proposed algorithm takes advantages of the strong global search ability and fast search speed of PSO to obtain the suboptimal solution.And then this suboptimal solution is transformed into the increment of initial pheromone in ACO.Finally,the exact solution is achieved via the positive feedback mechanism of ACO.Simulation results demonstrate that the proposed fusion algorithm,compared with ACO,generates the logistics distribution routing quickly and effectively,gains faster optimization speed and better convergence accuracy,and thus controls the cost of logistics distribution more reasonably.