摘要
对模糊需求信息条件下的车辆路径问题进行策略分析,提出解决此类问题的改进蚁群算法。采用多蚁群协作,修改信息素更新规则,根据收敛要求动态调整主要参数等对蚁群算法进行改进,应用该方法解决机会约束策略和可能性策略下的模糊需求车辆路径问题。通过采用模拟实际需求的方式评价各种策略得到的先验路径优劣。实验结果证明了改进算法对优化模糊需求车辆问题非常有效。
Based on the analysis of strategy for solving vehicle routing problem with fuzzy demands （VRPFD）, an improved ant colony algorithm was proposed. In this advanced algorithm, multi-ant colonies collaborated, the state transition rides were modified, and the parameters were adjusted according to the convergent requirements. It was applied to solve VRPFD under opportunity restriction and possibility strategy. The real demands based on statistical simulation were used to appraise the prior routing Experimental results show that the algorithm is feasible and effective for VRPFD.
出处
《计算机应用》
CSCD
北大核心
2006年第11期2639-2642,2660,共5页
journal of Computer Applications
基金
教育部留学归国人员基金项目
关键词
蚁群算法
模糊逻辑
模糊可能性
模糊需求
车辆路径问题
ant colony algorithm
fuzzy logic
fuzzy possibility
fuzzy demands
vehicle routing problem
