Design of emergency evacuation instruction system for multi-store buildings based on improved ant colony algorithm

Aiming at the problems of low efficiency and difficulty in emergency evacuation of multi-store buildings, this paper proposes an emergency evacuation path planning method for multi-store buildings based on ant colony algorithm. This method improves the heuristic function, takes into account the environmental impact of fire and the behavior characteristics of evacuees, and overcomes the blindness of ant colony algorithm. The adaptive pheromone updating strategy based on the complexity of obstacles effectively compensates the shortcomings of traditional ant colony algorithm such as slow convergence speed and easy to fall into local optimality, so that the improved ant colony algorithm can converge faster in simple building structures and find a better path in complex building structures. The improved ant colony algorithm is applied to the emergency evacuation model of multi-store buildings to realize the spatial optimal emergency evacuation path planning. The simulation results show that the improved ant colony algorithm can efficiently plan the optimal path and improve the safety and efficiency of emergency evacuation of multi-store buildings.

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Publication Details

Published
2024-11-08
DOI
https://doi.org/10.1117/12.3049490
Primary Topic
Evacuation and Crowd Dynamics
Type
article
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Design of emergency evacuation instruction system for multi-store buildings based on improved ant colony algorithm

Honghai An, B Zhang, Yuman Liu, Q. Liu et al.
Evacuation and Crowd Dynamics
article

Design of emergency evacuation instruction system for multi-store buildings based on improved ant colony algorithm

Honghai An, B Zhang, Yuman Liu, Q. Liu, Nan Chen, Jianjun Cui, Haolin Li, Haoran Xu
article en

Abstract

Aiming at the problems of low efficiency and difficulty in emergency evacuation of multi-store buildings, this paper proposes an emergency evacuation path planning method for multi-store buildings based on ant colony algorithm. This method improves the heuristic function, takes into account the environmental impact of fire and the behavior characteristics of evacuees, and overcomes the blindness of ant colony algorithm. The adaptive pheromone updating strategy based on the complexity of obstacles effectively compensates the shortcomings of traditional ant colony algorithm such as slow convergence speed and easy to fall into local optimality, so that the improved ant colony algorithm can converge faster in simple building structures and find a better path in complex building structures. The improved ant colony algorithm is applied to the emergency evacuation model of multi-store buildings to realize the spatial optimal emergency evacuation path planning. The simulation results show that the improved ant colony algorithm can efficiently plan the optimal path and improve the safety and efficiency of emergency evacuation of multi-store buildings.

Jilin Electric Power Research Institute (China) (CN), Changchun University of Technology (CN)
Sustainable cities and communities
Openalex Percentile: Top 73%
Evacuation and Crowd Dynamics
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