Abstract: Aiming at the problems such as long computing time and easy to fall into local best for ant colony algorithm in practical application, a new optimization parallel ant colony algorithm with particle swarm features is proposed. Combined with other relevant algorithms, this one is also applied in the case of logistics alliance vehicle routing. Experimental results show this algorithm has better performance in view of decreasing computing time and avoiding early maturing phenomenon.

Key words: ant colony algorithm, optimization particle swarm algorithm, parallel algorithm, logistics alliance

摘要： 针对蚁群算法在实际应用中存在的计算时间较长、容易陷入局部最优等问题，提出一种新的具有粒子群特征的优化并行蚁群算法，并将该算法与其他相关算法相结合，共同用于物流联盟车辆调度实例中。实验结果表明，该算法在减少计算时间以及避免早熟现象等方面具有较高的性能。

关键词: 蚁群算法, 粒子群优化算法, 并行算法, 物流联盟

CLC Number: 

• TP301.6