Population evacuation path optimization based on potential field ant colony and extended cellular automata

An effective safety evacuation program is an important basis for safeguarding the lives of people, and reasonable planning of evacuation routes is of great significance for formulating personnel evacuation plans. This article considers the global search ability of the ant colony algorithm and the local search ability of the artificial potential field. The artificial potential field is integrated into the ant colony algorithm, and combined with the extended Moore type cellular automata, an extended cellular automata model based on the potential field ant colony algorithm is proposed to optimize the calculation of personnel evacuation and path planning in the evacuation area. Analyze the performance of different algorithms in planning path smoothness, total path length, and calculation time from the same location in single exit and multi exit evacuation areas. And to verify the effectiveness of the algorithm, we use part of a teaching building as an evacuation scenario. The results show that combining the potential field ant colony algorithm with the extended Moore type cellular automata for path planning can reduce the number of invalid nodes and redundant turning points in the shortest path, improve the smoothness of the path, improve planning efficiency, and provide a design basis for emergency evacuation of buildings.