基于AACGA算法的校园防疫配送路径优化  

Optimization of Campus Epidemic Prevention Distribution Route Based on AACGA Algorithm

作　　者：郭文强[1] 杜正毅 李嫔 GUO Wen-qiang;DU Zheng-yi;LI Pin(Xinjiang University of Finance and Economics,Urumqi Xinjiang 830012,China)

机构地区：[1]新疆财经大学,新疆乌鲁木齐830012

出　　处：《计算机仿真》2023年第1期143-148,310,共7页Computer Simulation

基　　金：国家自然科学基金项目(61941205);天山雪松项目(2020XS07);新疆财经大学研究生科研创新项目(XJUFE2020K0043)。

摘　　要：疫情期间高校需要进行防疫物资配送,针对这一问题,基于传统蚁群算法(ACO)中的缺陷,提出一种自适应蚁群遗传算法(AACGA),建立车辆路径规化模型,通过分段更新信息素,自适应调整概率转移参数,并融入遗传算子进行优化,加强了搜索能力,有效的使算法跳出局部最优解。以新疆22所高校为例进行优化仿真测试,最终实验结果表明,在车辆配送成本以及收敛速度上,上述算法较之于其它算法有着明显的提升,从而验证了该算法的有效性。During the epidemic period, universities need to deliver epidemic prevention materials. To solve this problem, based on the shortcomings of the traditional ant colony algorithm(ACO),this paper proposes an adaptive ant colony genetic algorithm(AACGA) to establish a vehicle path planning model. By updating pheromones in segments, adaptive adjustment of probability transfer parameters, and integration of genetic operators, the search ability of the method is strengthened, and the algorithm can effectively jump out of the local optimal solution. This paper takes 22 universities in Xinjiang as an example to conduct optimization simulation tests. The results show that the algorithm has a significant improvement over other algorithms in terms of distribution cost and convergence speed, thus verifying the effectiveness of the algorithm.

关 键 词：自适应 遗传算子 车辆路径规划 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]