Research On Evacuation Roptimization Based On Extended Cellular Automat

An effective safety evacuation plan is an important basis for ensuring the safety of personnel inside buildings,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 automaton,optimization calculations are carried out for personnel evacuation and path planning in specific areas.Analyzed the impact of exit layout and personnel psychology on personnel evacuation,and conducted experimental analysis and comparison on individual evacuation paths,length of individual evacuation paths,and total length of all personnel evacuation planned by different algorithms.The main research work is as follows:By analyzing the neighborhood and movement rules of the extended Moore type cellular automata,an evacuation model based on the extended Moore type cellular automata is constructed to accelerate cell state search and reduce evacuation time steps.The evacuation problem in a specific area is simulated,and experimental data shows that the more dispersed the exit positions and the larger the exit width,the more conducive it is to evacuation,A certain proportion of conformity psychological personnel can accelerate evacuation,and a 24% proportion of conformity personnel is the most conducive to evacuation.In response to the problem of slow path planning and easy deadlock in ant colony algorithm,the breadth first search algorithm is used to calculate the danger level of the evacuation area.The danger level is added to the heuristic function of traditional ant colony algorithm,and trap cells are set to solve the deadlock problem.Combining the improved ant colony algorithm with cellular automata,the expanded Moore type cellular automata model reduces the average path length planned by 16% to 19% in the same location and multiple exits compared to the cellular automata model.To address the issue of non convergence and non optimal search paths in optimizing ant colony algorithms,artificial potential energy fields are integrated into the ant colony algorithm.A cellular automata model based on potential field ant colony algorithm and an extended Moore type cellular automata model based on potential field ant colony algorithm are established.By analyzing the evacuation paths at the same location and the total length of all evacuation paths,it can be concluded that the expanded Moore type cellular automata model based on potential field ant colony algorithm converges the fastest,the planning path is the shortest,and the calculation time of this model is 13.85% less than that of the cellular automata model based on optimized ant colony algorithm,and 10.20% less than that of the cellular automata model based on potential field ant colony algorithm.And based on actual scenarios,establish evacuation models and select different locations for path planning.Through comparative analysis,the extended Moore type cellular automata model based on potential field ant colony algorithm proposed in this article outperforms the other two models in path planning.