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8 November 2024 Design of emergency evacuation instruction system for multi-store buildings based on improved ant colony algorithm
Jian Cui, Banghe Zhang, Nan Chen, Quanting Liu, Haoran Xu, Yuman Liu, Hongchen An, Haolin Li
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Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 134160F (2024) https://doi.org/10.1117/12.3049490
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China
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.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
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Jian Cui, Banghe Zhang, Nan Chen, Quanting Liu, Haoran Xu, Yuman Liu, Hongchen An, and Haolin Li "Design of emergency evacuation instruction system for multi-store buildings based on improved ant colony algorithm", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 134160F (8 November 2024); https://doi.org/10.1117/12.3049490
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KEYWORDS
Buildings
3D modeling
Computer simulations
Fire
Design
Mathematical optimization
Modeling
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