摘要
以地震为例,针对灾后车辆路径优化问题的特征和需求,研究了救援通行时间、道路风险和道路付出成本等多目标的评估方法,以此为基础建立了震后车辆路径优化问题的多目标优化模型。由于普通蚁群算法在求解车辆路径问题过程中易陷入局部最优解,为此,设计了一种改进的遗传蚁群系统混合算法。通过引入遗传算法的变异算子增强算法的全局搜索能力,采用最大最小蚁群算法的实现机制来优化阶段最优解的子路径。实例仿真结果表明,该模型和算法是可行的,且效率和表现优于使用单一算法。
According to demand and characteristics of vehicle routing problem （VRP）after an earthquake,a multi-obj ective op-timization model was established,which treated the path time,path cost and path risk as the optimization target.Assessment methods of the optimization target were discussed.Because ant colony system （ACS）algorithm was easy to fall into a local opti-mal,an improved genetic ant colony hybrid algorithm was proposed.The algorithm expanded the scope of solution space and im-proved the global search ability by importing genetic mutation operator,optimized the stage optimal solution further by max-min ant colony algorithm mechanism.Results of the simulation indicated the proposed model and hybrid algorithm were feasible and had better efficiency and optimization performance than simple algorithm.
出处
《计算机工程与设计》
CSCD
北大核心
2014年第7期2526-2530,2535,共6页
Computer Engineering and Design
基金
重庆市科委自然科学基金计划基金项目(cstcjjA00021)
重庆市教委科技基金项目(KJ120427)
关键词
赈灾
车辆路径问题
混合
遗传算法
蚁群算法
disaster relief
vehicle routing problem （VRP）
hybrid
genetic algorithm
ant colony algorithm
