Aiming at the problems of slow convergence speed, low solution accuracy, and easy to fall into local optimum?in the lateriteration of particle swarm optimization,we firstly adopt a simplified particle swarm optimization algorithm that only uses the positionterm to control the evolution direction of particles,so as to avoid the?
problem of particle divergence caused by excessive particle speed.Secondly,the linear inertia weight that adaptively changes with the increase of the number of iterations is introduced to eliminate theinfluence of?
the inertial component,and the Levy flight strategy is introduced to change the particle position to help the particle escapefrom the local optimum. Finally,through four test function,we compare the performance of?
fixed-weight particle swarm optimization,standard particle swarm optimization and improved algorithms. Experiments show that the improved algorithm has improved convergencespeed,accuracy and stability. After verifying the effectiveness of the improved algorithm,the improved algorithm is used to solve the userequilibrium model of a single OD to a multi-path road network and compared with the results of the standard particle swarm optimizationalgorithm. The results of the improved algorithm are more stable and balanced. The feasibility of the improved algorithm is verified.