August 26, 2025Open Access

Multi-Objective Optimization Model for Emergency Evacuation Based on Adaptive Ant Colony Algorithm

Key Points

The model establishes a multi-objective optimization for evacuation paths, balancing congestion and time.
Primary outcomes are reduced congestion degree and quicker evacuation time during emergencies, significantly improving management.
An adaptive quantum ant colony algorithm was compared to traditional methods, showing enhanced performance in emergencies.
The approach considers psychological behavior, offering a holistic view for safe and efficient evacuation strategies.

Abstract

Evacuation in public places under emergency situations represents a significant area of management research. With the rapid development of the railway industry, the evacuation of railway stations has gradually attracted attention. This article employs the minimization of congestion degree and total evacuation time as primary objectives. In addition, the psychological behavior of individuals and the impact of congestion are sufficiently considered. Moreover, an adaptive Cauchy mutation operator is adopted for flexible population diversity. As a result, a multi-objective optimization model for the evacuation paths is established, with an improved adaptive quantum ant colony algorithm, and a comparison between the model based on adaptive quantum ant colony algorithm and the traditional ant colony model is made.

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Cite This Study

Yuan et al. (Tue,) studied this question.

synapsesocial.com/papers/68af63ddad7bf08b1eae3e1c https://doi.org/https://doi.org/10.3390/ai6090203

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