In order to solve the problem of finding the best evacuation route quickly and effectively, in the event of an accident, a novel evacuation route planning method is proposed based on Genetic Algorithm and Simulated Annealing algorithm in this paper. On the one hand, the simulated annealing algorithm is introduced and a simulated annealing genetic algorithm is proposed, which can effectively avoid the problem of the search process falling into the local optimal solution. On the other hand, an adaptive genetic operator is designed to achieve the purpose of maintaining population diversity. The adaptive genetic operator includes an adaptive crossover probability operator and an adaptive mutation probability operator. Finally, the path planning simulation verification is carried out for the genetic algorithm and the improved genetic algorithm. The simulation results show that the improved method has greatly improved the path planning distance and time compared with the traditional genetic algorithm.

The optimal evacuation route in emergency evacuation can further reduce casualties. Therefore, path planning is of great significance to emergency evacuation. Aiming at the blindness and relatively slow convergence speed of ant colony algorithm path planning search, an improved ant colony algorithm is proposed by combining artificial potential field and quantum evolution theory. On the one hand, the evacuation environment of pedestrians is modeled by the grid method. Use the potential field force in the artificial potential field, the influence coefficient of the potential field force heuristic information, and the distance between the person and the target position in the ant colony algorithm to construct comprehensive heuristic information. On the other hand, the introduction of quantum evolutionary theory. The pheromone is represented by quantum bits, and the pheromone is updated by quantum revolving door feedback control. In this way, it can not only reflect the high efficiency of quantum parallel computing, but also have the better optimization ability of ant colony algorithm. A large number of simulation experiments show that the improved ant colony algorithm has a faster convergence rate and is more effective in evacuation path planning.

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PurposeThe rapid evacuation of personnel in emergency situations is of great significance to the safety of pedestrians. In order to further improve the evacuation efficiency in emergency situations, this paper proposes a pedestrian evacuation model based on improved cellular automata based on microscopic features.Design/methodology/approachFirst, the space is divided into finer grids, so that a single pedestrian occupies multiple grids to show the microscopic behavior between pedestrians. Second, to simulate the velocity of pedestrian movement under different personnel density, a dynamic grid velocity model is designed to establish a linear correspondence relationship with the density of people in the surrounding environment. Finally, the pedestrian dynamic exit selection mechanism is established to simulate the pedestrian dynamic exit selection process.FindingsThe proposed method is applied to single-exit space evacuation, multi-exit space evacuation, and space evacuation with obstacles, respectively. Average speed and personnel evacuation decisions are analyzed in specific applications. The method proposed in this paper can provide the optimal evacuation plan for pedestrians in multiple exit and obstacle environments.Practical implications/Social implicationsIn fire and emergency situations, the method proposed in this paper can provide a more effective evacuation strategy for pedestrians. The method proposed in this paper can quickly get pedestrians out of the dangerous area and provide a certain reference value for the stable development of society.Originality/valueThis paper proposes a cellular automata pedestrian evacuation method based on a fine grid velocity model. This method can more realistically simulate the microscopic behavior of pedestrians. The proposed model increases the speed of pedestrian movement, allowing pedestrians to dynamically adjust the speed according to the specific situation.