A Novel Swarm Intelligence Algorithm for the Evacuation Routing Optimization Problem

This paper presents a novel swam intelligence optimization algorithm that combines the evolutionary method of Particle Swarm Optimization (PSO) with the filled function method in order to solve the evacuation routing optimization problem. In the proposed algorithm, the whole process is divided into three stages. In the first stage, we make use of global optimization of filled function to obtain optimal solution to set destination of all particles. In the second stage, we make use of the randomicity and rapidity of PSO to simulate the crowd evacuation. In the third stage, we propose three methods to manage the competitive behaviors among the particles. This algorithm makes an evacuation plan using the dynamic way finding of particles from both a macroscopic and a microscopic perspective simultaneously. There are three types of experimental scenes to verify the effectiveness and efficiency of the proposed algorithm: a single room, a 4-room/1-corridor layout, and a multi-room multi-floor building layout. The simulation examples demonstrate that the proposed algorithm can greatly improve upon evacuation clear and congestion times. The experimental results demonstrate that this method takes full advantage of multiple exits to maximize the evacuation efficiency.