Multi-Objective Optimization for Emergency Evacuation Routing in Shopping Mall Fires

To improve fire emergency response capacity in public buildings, this paper proposes a multi-objective emergency evacuation path optimization method. The method is based on an improved genetic algorithm for a mall fire environment. The scheme presents a multi-objective optimization. It considers both evacuation time and crowd congestion. The scheme incorporates physical fire parameters. These parameters include visibility, temperature, and CO concentration. These factors influence evacuation speed. The scheme also introduces a panic factor. This factor accounts for the impact of human psychological behavior on evacuation efficiency during a fire. This study combines SMAA with the fire–behavior–congestion–path coupling framework. In addition, this study solves the model using a genetic algorithm. This algorithm includes several improvements over the conventional genetic algorithm. This study uses a shopping mall as a case study. It simulates the fire spread process using PyroSim software. This simulation provides fire scene data. Experimental results show that the multi-objective optimization scheme improves passage efficiency. It also safeguards pedestrian safety. These results demonstrate the effectiveness of the proposed multi-objective algorithm.