摘要
合理规划救援路径可缩短救援行程时间,促使应急救援力量迅速到达现场,提升应急救援效率。文中基于城市道路交通运行特征与交通环境动态演变规律,提出了一种用于求解最优救援路径的可扩展协同进化算法(ECEA)。ECEA算法建立了协同优化机制,即路径优化过程与道路交通环境演变协同进行,并可灵活选择算法寻优搜索范围,提升可行解的数量与质量。实验结果表明:在数据获取能力受限情况下,该算法在行程时间及其鲁棒性方面均优于定时协同优化算法(TCEPO)与在线优化方法(OLRO),可有效缩短行程时间,进而提升应急救援效率与救援效果。
Reasonable path planning can shorten the travelling time to ensure that rescue forces can arrive at the scene in time and improve the efficiency of emergency rescue.Based on the urban road traffic characteristics and the dynamic evolution of traffic environments,this paper proposed an extended co-evolutionary algorithm(ECEA)to calculate the optimal rescue path.The ECEA establishes a co-evolutionary optimization mechanism,which means that the path planning process co-evolves with the evolution of traffic environments.Meanwhile,ECEA can flexibly select the search scope to improve the number and quality of alternative solutions.Experimental results show that ECEA outperforms timing co-evolutionary algorithm(TCEPO)and Online re-optimization(OLRO)both in the travelling time and robustness under the condition of limited data,thereby improving emergency rescue efficiency.
作者
温惠英
林译峰
吴昊书
蒋晗
吴嘉彬
WEN Huiying;LIN Yifeng;WU Haoshu;JIANG Han;WU Jiabin(School of Civil Engineering and Transportation,South China University of Technology,Guangzhou 510640,Guangdong,China;College of Forestry and Landscape Architecture,South China Agricultural University,Guangzhou 510640,Guangdong,China)
出处
《华南理工大学学报（自然科学版）》
EI
CAS
CSCD
北大核心
2021年第10期1-10,共10页
Journal of South China University of Technology(Natural Science Edition)
基金
国家自然科学基金资助项目(51578247,71701070)。
关键词
应急救援
交通环境演变
路径规划
协同优化
emergency rescue
traffic environment evolution
path planning
co-evolutionary optimization
