摘要
针对停车场停车最优路径规划问题,提出了基于改进型蚁群算法的自适应停车引导模型。以原有信息素更新为基础,设计下一步潜在节点状态转移策略,引入停车路径动态自适应度,进一步缩小蚂蚁搜索范围,合理规划最优停车路径。对比仿真实验表明:改进型自适应蚁群算法求解效率和质量有明显优势,实现了停车最优路径规划与选择,减少了停车时间,提高了安全性,具有很强的实用价值。
In view of the problem of optimal path planning for parking lots, an adaptive parking guidance model based on improved ant colony algorithm is proposed. Based on the updating of the original pheromone, the next potential node state transition strategy is designed, and the dynamic adaptive degree of parking path is introduced to further narrow the scope of ant search and reasonably plan the optimal parking path. The simulation results show that the improved adaptive ant colony algorithm has obvious advantages in solving efficiency and quality, realizing the optimal path planning and selection of parking, reducing the parking time and improving the safety, and has strong practical value.
作者
晏勇
雷航
梁潘
YAN Yong;LEI Hang;LIANG Pan(College of Electronic Information and Automation,Aba Teachers University,Aba 623002,China;School of Information and Software Engineering,University of Electronic Science and Technology of China,Chengdu 610054,China;College of Sergeant Management,Chengdu Aeronautical Vocational and Technical College,Chengdu 610100,China)
出处
《实验技术与管理》
CAS
北大核心
2020年第3期80-82,138,共4页
Experimental Technology and Management
基金
国家自然科学基金项目(61373163)
四川教育厅自然科学重点项目(17ZA0002)。
关键词
停车
最优路径
改进蚁群算法
状态转移策略
自适应度
parking
optimal path
improved ant colony algorithm
state transition strategy
adaptive degree
