Research on Multi-Objective Path Planning for Emergency Evacuation in Subway Stations Using an Integrated and Improved Ant Colony-Genetic Algorithm

To address the safety and efficiency challenges in planning emergency evacuation routes for personnel in complex environments, this study proposes an integrated and improved ant colony optimization (ACO) with a genetic algorithm (GA). First, an emergency evacuation route planning model for subway incidents is constructed by optimizing evacuation time, route risk, and the passenger panic index. Then, the ant colony algorithm is enhanced by assigning pheromones to each objective and optimizing the state transition probabilities, which helps avoid premature convergence on local optima. Simultaneously, a GA is employed to conduct a global search and generate an initial population, which serves as the initial pheromone for the ACO. This approach achieves the integration of ACO and GA, enabling them to synergistically leverage the advantages of global and local search. Finally, an evacuation simulation was conducted using a specific subway station as an example, and the results were compared with those of traditional algorithms. The results indicate that the proposed algorithm can find the optimal solution for all evacuation routes and significantly improve convergence speed and global search capabilities. In simulations across different hazard development stages, the proposed integrated method outperforms basic ACO and SSA by accounting for evacuation time, safety, and crowd panic to yield optimal routes.