Optimization Design of Particle Swarm Optimization for Intelligent Control of Rail Transit

Affected by the train delay, there will be some unfavorable factors, which will reduce passengers' satisfaction with the service quality of the line, limit the ability to use the line and increase the train operation cost. Improving operation efficiency and reducing the delay caused by train delay are the primary problems to be solved in train dispatching. The train operation process is characterized by nonlinearity, high complexity and a large number of uncertainties, and the traditional control methods often require higher accuracy of mathematical models, so it is difficult for traditional control methods to achieve ideal results in this very complicated process. Aiming at the intelligent control model of rail transit, based on the traditional PSO(Particle swarm optimization) algorithm, this paper uses the idea of GA(genetic algorithm) to combine GA with PSO algorithm to get GA _ PSO algorithm. In GA _ PSO algorithm, the better offspring from the crossover of better parents replaces the inferior offspring, which accelerates the convergence of the algorithm to some extent and can obtain the particle near-optimal solution in fewer iterations. The simulation results show that a new adjustment plan is finally produced after the adjustment of GA_PSO algorithm, which solves the problem of single car and train group delay well. Therefore, it is feasible to use GA _ PSO algorithm to solve the intelligent control problem of rail transit.