Evacuation strategy for high-rise building fires based on Spotted Hyena Optimizer and A*

Tong  Wang

doi:10.6180/jase.202501_28(1).0013

Evacuation strategy for high-rise building fires based on Spotted Hyena Optimizer and A*

Computer Science and Information Engineering

Tong WangThis email address is being protected from spambots. You need JavaScript enabled to view it.

Hebei Software Institute, Baoding, Hebei, 071000,China

Received: January 29, 2024
Accepted: February 21, 2024
Publication Date: March 27, 2024

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202501_28(1).0013

A Spotted Hyena Optimizer (SHO) dynamic path planning method is proposed for high-rise building fires (referred to as SHOHDM). This method aims to address several critical issues in high-rise building fire scenarios, including complex escape path planning, extended escape times, congestion and stampedes at exits, and limited terminal computing power.To achieve these objectives, we introduce a SHO-based dynamic escape path planning mechanism. This mechanism is designed to expedite escape times and enhance path safety. Additionally, we propose a dynamic matching degree based on the A* algorithm to effectively allocate crowd density at exits, thereby improving escape efficiency.Furthermore, this article presents a fire escape model based on the Internet of Things (IoT). This model facilitates communication between devices and between devices and the cloud through the establishment of an internal network. By utilizing cloud processing instead of relying solely on terminal computing, we significantly reduce the overall running time.To evaluate the effectiveness of our approach, we conducted comparative experiments on multiple sets of single-target and multi-objective global path planning scenarios using different grid map specifications. The results demonstrate that our algorithm reduces escape times by 11.4% and 15.5%, respectively, while maintaining high precision and robustness in general scenarios.

Keywords: A* algorithm;Path Planning; Fire; Spotted Hyena Optimizer

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