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山东大学学报 (工学版) ›› 2021, Vol. 51 ›› Issue (3): 1-6.doi: 10.6040/j.issn.1672-3961.0.2020.259

• 机器学习与数据挖掘 •    下一篇

基于多目标驱动人工蜂群算法的疏散仿真模型

宗欣露(),杜佳圆   

  1. 湖北工业大学计算机学院, 湖北 武汉 430068
  • 收稿日期:2020-06-30 出版日期:2021-06-20 发布日期:2021-06-24
  • 作者简介:宗欣露(1981—)，女，河北邯郸人，副教授，博士，主要研究方向为智能系统与智能计算. E-mail：zongxinlu@126.com
  • 基金资助:
    国家自然科学基金资助项目(61772180);国家自然科学基金资助项目(61602162);湖北工业大学绿色工业引领计划资助项目(YXQN2017002)

Evacuation simulation model based on multi-target driven artificial bee colony algorithm

Xinlu ZONG(),Jiayuan DU   

  1. School of Computer Science and Technology, Hubei University of Technology, Wuhan 430068, Hubei, China
  • Received:2020-06-30 Online:2021-06-20 Published:2021-06-24

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摘要：

提出一种基于多目标驱动蜂群算法的疏散仿真模型。在人工蜂群算法的基础上, 对跟随蜂设置视野搜索半径, 将视野范围内适应值最优个体作为其视觉引领蜂, 从而减少跟随蜂选择引领蜂的盲目性。提出多目标驱动人工蜂群算法, 即由惯性引领蜂、全局最优蜂、个体历史最优蜂、视觉引领蜂共同对跟随蜂的移动方向进行引导, 从而使跟随蜂的移动路径得到进一步优化。试验结果表明, 多目标驱动人工蜂群算法疏散效率较高, 在疏散总人数较多的情况下性能更优, 且人员分布更为合理。本研究的模型和算法能够有效提高疏散效率, 适合多障碍物场景下的疏散问题。

关键词: 疏散模型, 人工蜂群算法, 多目标驱动, 元胞自动机, 视觉引领

Abstract:

An evacuation simulation model based on multi-target driven artificial bee colony algorithm was presented. Based on the artificial bee colony algorithm, the visual field was used for each following bee to choose the individual with the best fitness value in the field as its visual leading bee and avoid blind choice. A multi-target driven artificial bee colony algorithm was proposed. The moving direction of each following bee was affected by multiple targets, including inertial leading bee, global optimal bee, historical optimal bee and visual leading bee. The experimental results showed that the multi-target driven artificial bee colony algorithm had higher efficiency and achieved better performance and more reasonable distribution in the case of larger number of evacuees. The model and algorithm presented could effectively improve evacuation efficiency and was suitable for the evacuation problem in multi-obstacle situation.

Key words: evacuation model, artificial bee colony algorithm, multi-target driving, cellular automata, visual leading

中图分类号: 

  • TP391

图1

Moore型邻域"

图2

MDABC算法流程图"

表1

不同参数设置下的结果"

ω c1 c2 c3 平均路径长度/m 疏散时间/s
1.0 1.0 1.0 1.0 26.070 29.5
2.0 1.0 1.0 1.0 26.585 32.5
1.0 2.0 1.0 1.0 26.055 29.0
1.0 1.0 2.0 1.0 25.915 28.5
1.0 1.0 1.0 2.0 25.960 29.0
1.0 1.0 1.0 3.0 25.835 28.0
1.0 1.0 1.0 4.0 25.820 28.5
1.0 1.0 1.0 5.0 25.950 28.5
1.0 1.0 1.0 1.5 26.070 29.5
0.7 1.0 1.0 2.0 26.075 29.5
0.5 1.0 1.0 2.0 25.965 28.5
0.2 1.0 1.0 2.0 26.005 29.0
1.0 2.0 1.0 1.0 26.000 29.0
1.0 3.0 1.0 1.0 26.035 29.0
1.0 1.0 3.0 1.0 26.140 29.5
1.0 1.0 5.0 1.0 26.120 30.0
1.0 1.5 1.5 2.0 25.915 28.5

图3

有或无视觉引领的疏散效率曲线"

图4

疏散场景"

图5

不同出口位置下的疏散时间与人数关系"

图6

4种方法的疏散效率"

图7

4种方法在不同人数下的疏散总时间比较"

图8

疏散仿真过程(t=20 s)"

1 KELLY R R , JULIAN A , ALCIDES S M , et al. Modelling building emergency evacuation plans considering the dynamic behaviour of pedestrians using agent-based simulation[J]. Safety Science, 2019, 113, 276- 284.
doi: 10.1016/j.ssci.2018.11.028
2 CAO S C , LIU X D , CHRAIBI M , et al. Characteristics of pedestrian's evacuation in a room under invisible conditions[J]. International Journal of Disaster Risk Red-uction, 2019, 41, 101295.
doi: 10.1016/j.ijdrr.2019.101295
3 HAGHANI M , SARVI M . Imitative (herd) behaviour in direction decision-making hinders efficiency of crowd evacuation processes[J]. Safety Science, 2019, 114, 49- 60.
doi: 10.1016/j.ssci.2018.12.026
4 LIU Q . A social force model for the crowd evacuation in a terrorist attack[J]. Physica A: Statistical Mechanics & Its Applications, 2018, 502, 315- 330.
5 KHAMIS N , SELAMAT H , ISMAIL F S , et al. Optimized exit door locations for a safer emergency evacuation using crowd evacuation model and artificial bee colony optimization[J]. Chaos, Solitons & Fractals, 2019, 131, 109505.
6 张鑫龙, 陈秀万, 李怀瑜, 等. 一种改进元胞自动机的人员疏散模型[J]. 武汉大学学报(信息科学版), 2017, 42 (9): 1330- 1336.
ZHANG X L , CHEN X W , LI H Y , et al. An improved cellular automata model for simulation pedestrian evacuation[J]. Geomatics and Information Science of Wuhan University, 2017, 42 (9): 1330- 1336.
7 李建建. 基于元胞遗传算法的避障路径规划[D]. 南昌: 南昌航空大学, 2012.
LI J J. Obstacle avoidance path planning based on cellular genetic algorithms[D]. Nanchang: Nanchang Hangkong University, 2012.
8 徐斌, 刘弘. 融合社会力与人工蜂群的人群疏散仿真方法[J]. 小型微型计算机系统, 2016, 37 (8): 1725- 1729.
doi: 10.3969/j.issn.1000-1220.2016.08.018
XU Bing , LIU Hong . Crowd evacuation simulation method combining social force with artificial bee colony[J]. Journal of Chinese Computer Systems, 2016, 37 (8): 1725- 1729.
doi: 10.3969/j.issn.1000-1220.2016.08.018
9 MING Z , NA T , VASILE P . Cellular artificial bee colony algorithm with Gaussian distribution[J]. Information Sciences, 2018, 462, 374- 401.
doi: 10.1016/j.ins.2018.06.032
10 CHEN X , TIANFIELD H , LI K J . Self-adaptive differential artificial bee colony algorithm for global optimization problems[J]. Swarm and Evolutionary Com-putation, 2019, 45, 70- 91.
doi: 10.1016/j.swevo.2019.01.003
11 HUANG K , ZHENG X , CHENG Y , et al. Behavior-based cellular automaton model for pedestrian dynamics[J]. Applied Mathematics & Computation, 2017, 292, 417- 424.
12 LI Y , CHEN M , ZHENG X , et al. Relationship between behavior aggressiveness and pedestrian dynamics using behavior-based cellular automata model[J]. Applied Mathematics and Computation, 2020, 371, 124941.
doi: 10.1016/j.amc.2019.124941
13 FU L , FANG J , CAO S . A cellular automaton model for exit selection behavior simulation during evacuation processes[J]. Procedia Engineering, 2018, 211, 169- 175.
doi: 10.1016/j.proeng.2017.12.123
14 FANG W F , YANG L Z , FAN W C . Simulation of bi-direction pedestrian movement using a cellular automata model[J]. Physica A, 2003, 321 (3): 633- 640.
15 NAJIMI M , GHAFOORI N , NIKOO M . Modeling chloride penetration in self-consolidating concrete using artificial neural network combined with artificial bee colony algorithm[J]. Journal of Building Engineering, 2018, 22, 216- 226.
16 KINATEDER M , COMUNALE B , WARREN W H . Exit choice in an emergency evacuation scenario is influenced by exit familiarity and neighbor behavior[J]. Safety Science, 2018, 106, 170- 175.
doi: 10.1016/j.ssci.2018.03.015
17 霍富昌, 姜志侠, 谢继轩, 等. 基于出口选择概率的修正社会力模型的行人疏散研究[J]. 长春理工大学学报(自然科学版), 2018, 41 (6): 128- 131.
doi: 10.3969/j.issn.1672-9870.2018.06.028
HUO Fuchang , JIANG Zhixia , XIE Jixuan , et al. Study on pedestrian evacuation based on modified social force model with exit choice probability[J]. Journal of Changchun University of Science and Technology (Natural Science Edition), 2018, 41 (6): 128- 131.
doi: 10.3969/j.issn.1672-9870.2018.06.028
18 ZONG X L, XIONG S W, XU H, et al. Space-time simulation model based on particle swarm optimization algorithm for stadium evacuation[C]//Proceedings of the 2014 IEEE Congress on Evolutionary Computation. New York, United States: Institute of Electrical and Electronics Engineers Inc., 2014: 194-201.
19 ZONG X L, DU J Y, LIU W, et al. Indoor emergency evacuation model based on artificial bee colony algorithm[C]//Proceedings of the 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications, New York, United States: Institute of Electrical and Electronics Engineers Inc., 2019: 18-23.
20 LIU H , XU B , LU D . A path planning approach for crowd evacuation in buildings based on improved artificial bee colony algorithm[J]. Applied Soft Computing, 2018, 68, 360- 376.
doi: 10.1016/j.asoc.2018.04.015
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