Study on Building Fire Evacuation Path Planning Based on Improved Ant Colony Algorithm

doi:10.16182/j.issn1004731x.joss.20-0978

Abstract

Abstract:

Aiming at the problem of dynamic planning of evacuation paths in comprehensive building fires, with the shortest escape time required for evacuees as the goal, considering the impact of fire products and crowd density on the evacuation speed of personnel, an evacuation path planning model based on improved ant colony algorithm is constructed. A evacuation network data model composed of an obstacle vertex grid is established, the inspiration function of the ant colony algorithm and the deadlock processing strategy are improved, the explosion operator in the fireworks algorithm is introduced to optimize the ant path, and a comprehensive building is used as an example for simulation experiments. The results show that the model can not only avoid evacuation paths through the dangerous area, but also adjust the evacuation path according to the construction environment and personnel distribution, which improves the safety of personnel evacuation path.

Key words: building fire, path optimization, ant colony algorithm, evacuation, fireworks algorithms

CLC Number:

TP391.9

Jiangtao Liang, Huiqin Wang. Study on Building Fire Evacuation Path Planning Based on Improved Ant Colony Algorithm[J]. Journal of System Simulation, 2022, 34(5): 1044-1053.

Figures/Tables 8

Fig. 1

Fig. 2

Table 1

Influence of main parameters on optimal path

参数	取值	路径长度/m
α	0.1	91.28
	0.5	90.64
	1	90.53
	1.5	90.53
$β$	0.5	91.41
	1	91.35
	5	90.53
	7	90.63
$ρ$	0.05	90.53
	0.1	90.53
	0.3	90.78
	0.5	91.15
Q	1	90.77
	5	90.53
	10	90.53
	15	90.53

Table 1

Fig. 3

Fig. 4

Table 2

Fig. 5

Table 3

References 17

[1]	Mirahadi F, McCabe B. EvacuSafe: Building Evacuation Strategy Selection Using Route Risk Index[J]. Journal of Computing in Civil Engineering (S0887-3801), 2020, 34(2): 04019051.
[2]	Yang J, Shi M, Han Z. Research Intelligent Fire Evacuation System Based on Ant Colony Algorithm and Mapx[C]//Seventh International Symposium on Computational Intelligence and Design. Hangzhou, China: IEEE, 2014: 2: 100-103.
[3]	李维刚, 王肖, 赵云涛, 等. 基于栅格法的钢厂无人天车调度系统[J]. 系统仿真学报, 2020, 32(4): 687-699.
	Li Weigang, Wang Xiao, Zhao Yuntao, et al. Unmanned Crane Dispatching System Based on Grid Method in Steel Works[J]. Journal of System Simulation, 2020, 32(4): 687-699.
[4]	史岩, 张立华, 董受全, 等. ACO算法和Bezier曲线优化的巡航导弹航路规划[J]. 系统仿真学报, 2020, 32(1): 122-129.
	Shi Yan, Zhang Lihua, Dong Shouquan, et al. Cruise Missile Path Planning Based on ACO Algorithm and Bezier Curve Optimization[J]. Journal of System Simulation, 2020, 32(1): 122-129.
[5]	Mirahadi F, Mccabe B Y. Evacusafe: A Real-Time Model for Building Evacuation Based on Dijkstra’s Algorithm[J]. Journal of Building Engineering (S2352-7102), 2021, 34: 101687.
[6]	曹祥红, 李欣妍, 魏晓鸽, 等. 基于Dijkstra-ACO混合算法的应急疏散路径动态规划[J]. 电子与信息学报, 2020, 42(6): 1502-1509.
	Cao Xianghong, Li Xinyan, Wei Xiaoge, et al. Dynamic Programming of Emergency Evacuation Path Based on Dijkstra-ACO Hybrid Algorithm[J]. Journal of Electronics & Information Technology, 2020, 42(6): 1502-1509.
[7]	Wu C H, Chen L C. 3D Spatial Information for Fire-Fighting Search and Rescue Route Analysis Within Buildings[J]. Fire Safety Journal (S0379-7112), 2012, 48: 21-29.
[8]	Hajjem M, Bouziri H, Talbi E G, et al. Intelligent Indoor Evacuation Guidance System Based on Ant Colony Algorithm[C]//14th International Conference on Computer Systems and Applications. Hammamet, Tunisia: IEEE, 2017: 1035-1042.
[9]	Goodwin M, Granmo O C, Radianti J. Escape Planning In Realistic Fire Scenarios with Ant Colony Optimisation[J]. Applied Intelligence (S0924-669X), 2015, 42(1): 24-35.
[10]	刘毅, 沈斐敏. 考虑灾害实时扩散的室内火灾疏散路径选择模型[J]. 控制与决策, 2018, 33(9): 1598-1604.
	Liu Yi, Shen Feimin. Route Selection Model in Indoor Evacuation Under Real Effect of Fire Spread[J]. Control and Decision, 2018, 33(9): 1598-1604.
[11]	赵江, 王晓博, 郝崇清, 等. 栅格图特征提取下的路径规划建模与应用[J]. 计算机工程与应用, 2020, 56(10): 254-260.
	Zhao Jiang, Wang Xiaobo, Hao Chongqing, et al. Path Planning Modeling and Application Based on Feature Extraction of Grid Graph[J]. Computer Engineering and Applications, 2020, 56(10): 254-260.
[12]	叶成豪, 刘月婵, 孙超, 等. 考虑火灾产物影响条件下的人员疏散仿真研究[J]. 中国安全科学学报, 2020, 30(6): 142-151.
	Ye Chenghao, Liu Yuechan, Sun Chao, et al. Simulation Study on Personnel Evacuation Considering Impacts of Fire Products[J]. China Safety Science Journal, 2020, 30(6): 142-151.
[13]	袁春燕, 王坤, 陈虹燕, 等. 基于人员心理-环境因素的火灾疏散速度修正方法研究[J]. 中国安全生产科学技术, 2020, 16(7): 112-118.
	Yuan Chunyan, Wang Kun, Chen Hongyan, et al. Study on Correction Method of Fire Evacuation Speed Based on Personnel Psychological and Environmental Factors[J]. Journal of Safety Science and Technology, 2020, 16(7): 112-118.
[14]	郑斌, 冉海潮, 孙丽华. 建筑火灾中人群智能疏散诱导系统的工程设计方法[J]. 建筑科学, 2014, 30(7): 98-102.
	Zheng Bin, Ran Haichao, Sun Lihua. Engineering Design Methods of Crowd Intelligent Evacuation Guidance System in Building Fire[J]. Building Science, 2014, 30(7): 98-102.
[15]	Dorigo M, Maniezzo V, Colorni A. Ant System: Optimization by a Colony of Cooperating Agents[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics) (S1083-4419), 1996, 26(1): 29-41.
[16]	Tan Y, Zhu Y C. Fireworks Algorithm for Optimization[C]//International Conference on Advances in Swarm Intelligence. Berlin: Springerlink, 2010: 355-364.
[17]	李志锟, 黄宜庆, 徐玉琼. 改进变步长蚁群算法的移动机器人路径规划[J]. 电子测量与仪器学报, 2020, 34(8): 15-21.
	Li Zhikun, Huang Yiqing, Xu Yuqiong. Path Planning of Mobile Robot Based on Improved Variable Step Size Ant Colony Algorithm[J]. Journal of Electronic Measurement and Instrumentation, 2020, 34(8): 15-21.

算法	路径长度/m	收敛迭代次数	运行时间/s
文献[17]	84.53	21	13.42
本文算法	83.23	17	13.50

算法	参数	起点终点
算法	参数	(2.5,50.5) (68.5,12.5)	(2.5,10.5) (40.5,69.5)
ACS	L/m	90.60	85.88
ACS	M/只	349.00	58.00
蚁群	L/m	96.99	87.28
蚁群	M/只	267.80	43.60
改进	L/m	90.52	85.40
蚁群	M/只	8.50	15.00