基于改进蚁群算法的物流配送路径优化  

Optimizing logistic distribution routing problem based on improved ant colony algorithm

作　　者：张维泽[1] 林剑波 吴洪森[3] 童若锋[1] 董金祥[1] 

机构地区：[1]浙江大学人工智能研究所,浙江杭州310027 [2]浙江金基电子有限公司,浙江杭州310013 [3]浙江警察学院基础部,浙江杭州310053

出　　处：《浙江大学学报（工学版）》2008年第4期574-578,597,共6页Journal of Zhejiang University：Engineering Science

基　　金：浙江省重大科技攻关资助项目(2005C13023)

摘　　要：建立了带约束条件的物流配送问题的数学模型,运用蚁群算法解决物流配送路径优化问题,将遗传算法的复制、交叉和变异等遗传算子引入蚁群算法,以提高算法的收敛速度和全局搜索能力;改进了信息素的更新方式,以提高蚁群算法的自适应性,使得算法在执行过程中能根据收敛和进展情况,相应地调整信息残留程度,从而提高收敛速度或全局搜索能力;引入了一种确定性搜索方法,加快启发式搜索的收敛速度.经过多次对比实验表明,使用改进的蚁群算法优化物流配送线路,可以有效而快速地求得问题的最优解或近似最优解.A mathematical logistic distribution model with constraints was constructed and an improved ant colony algorithm was proposed to solve the route optimization problem of logistic distribution. Several genetic operators, such as reproduction, crossover and mutation, were introduced into the ant colony algorithm to enhance the converging rate and global search capability. To improve the self-adaptability of the algorithm, pheromone updating strategy was modified by adjusting the pheromone residual according to the progress of the algorithm convergence. A deterministic search approach was introduced into the algorithm to accelerate the converging rate of the heuristic method. Experimental results show that the optimal or nearly optimal solutions to the logistic distribution routing can be quickly obtained by the improved ant colony algorithm.

关 键 词：物流配送 路径优化 蚁群算法 蚁群系统 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程]