融合改进蚁群和粒子群算法的路径搜索应用  

Application of Improved Fused ACO and PSO Algorithms in Vehicle Routing Search

作　　者：杜博[1] 夏春蕾[1] 戴曙光[1] 

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《电子科技》2016年第9期4-6,135,共4页Electronic Science and Technology

基　　金：国家自然科学基金资助项目(61170277);上海市教委科研创新重点基金资助项目(12zz137);上海市一流学科建设基金资助项目(S1201YLXK)

摘　　要：针对车辆路径搜索对其计算质量和效率要求较高问题,且原始蚁群算法和标准粒子群算法均存在局部优先解、停滞以及收敛速度较慢等缺陷,提出一种融合改进的蚁群和粒子群路径搜索算法。在融合算法前期提高粒子群算法收敛速度,利用其进行粗搜索,后期利用改进的蚁群算法进行细搜索。通过仿真分析表明,融合后的改进算法在路径规划和计算效率上均有较大提升。The ant colony and particle swarm optimization have the disadvantages of local preferred solution, stagnation and low convergence speed. A fusion of improved ant colony and particle swarm algorithm is proposed to meet the high quality and efficiency requirements of vehicle routing search. , use the coarse search is used in the early stage, and the improved ant colony algorithm for the following fine search. Simulation shows that the improved algo- rithm significant improves the efficiency of path planning and calculation.

关 键 词：路阻模型 融合算法 路径搜索 仿真分析 

分 类 号：TP273[自动化与计算机技术—检测技术与自动化装置]