基于ACSAAD算法的改进冠状病毒群体免疫优化有能力车辆路径问题

Yuqing Gao, Ruey-Maw Chen
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摘要

有能力车辆路径问题(CVRPs)被称为NP-Hard问题,其目标是在不违反约束条件的情况下,找到成本最小的最优路径规划。针对CVRPs问题,设计了一种基于关联客户节约算法的改进冠状病毒群体免疫优化算法MCASA。首先,修改单个解决方案更新,使探索更加灵活。其次,提出了一种新的ACSAAD保存算法来调整客户访问顺序。最后,设计了种群状态更新机制,防止CHIO快速进入开发阶段。在CVPLIB的CVRPs数据集上测试了三个不同的尺度实例。仿真结果表明,MCASA能够找到被测实例的最优解,ARPD不大于0.2,表明MCASA能够有效地求解cvrp问题。
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Modified Coronavirus Herd Immunity optimization with an ACSAAD Algorithm for Capacitated Vehicle Routing Problems Vehicle Routing Problems
The capacitated vehicle routing problems (CVRPs) are well-known as NP-Hard, which aims to find the optimal route planning with the least cost without violating the constraints. A modified coronavirus herd immunity optimization with an associative customers savings algorithm, named MCASA, is designed to solve CVRPs. First, the individual solution update is modified to make the exploration more flexible. Second, a new saving algorithm, named ACSAAD, is suggested to adjust the customer visit order. Finally, a population state update mechanism is designed to prevent the CHIO from entering the exploitation stage quickly. Three different scale instances on the CVRPs dataset of CVPLIB were tested. The simulation results show that the MCASA can find the optimal solution for the tested instances, with ARPD no more than 0.2, indicating that the MCASA can effectively and efficiently solve CVRPs.
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