针对传统停车场管理系统人工成本高、管理难度大的问题，提出了一种基于信息素优化蚁群算法(Ant Colony Algorithm)的停车场系统；该系统以STM32作为主控制器，终端节点负责数据收集,利用NB-IoT实现数据上传,采用手机APP和云平台对终端节点远程监控,采用粒子群算法为蚁群算法提供迭代初期值指导后，增强了蚁群算法全局搜索能力，改进蚁群算法明显缩短了停车的最短哈密顿回路距离；通过搭建停车场管理系统对该方法的有效性进行验证，该系统可以明显减少用户停车的时间，缩短用户停车距离，满足自动化智能化的生活需要。
As the traditional parking space management system has problems of high labor cost and great difficulty in management, a parking system based on pheromone optimization ant colony algorithm is proposed. The system uses STM32 as the main controller, the terminal sensor node is responsible for collecting data, and NB-IoT is used to upload data. Mobile phone APP and cloud platform can be used to realize remote monitoring of the terminal node. Particle swarm optimization algorithm is used to provide pheromone guidance for the ant colony algorithm at the beginning of iteration, which enhances the global search ability of the ant colony algorithm, and the improved ant colony algorithm obviously shortens the shortest Hamilton loop distance of parking. The effectiveness of the method is verified by building a parking lot management system, and the experimental results show that the system can significantly reduce the parking time of users, shorten the parking distance of users, and meet the needs of automatic and intelligent life.
孙霞, 胡小飞, 张昕, 黄新洁, 王成辰.基于信息素优化蚁群算法下的停车场系统设计[J].重庆工商大学学报（自然科学版）,2022,39(2):1-7
SUN Xia, HU Xiao-fei, ZHANG Xin, HUANG Xin-jie, WANG Cheng-chen. Design of Parking Lot System Based on Pheromone Optimization Ant Colony Algorithm[J]. Journal of Chongqing Technology and Business University(Natural Science Edition）,2022,39(2):1-7