Train Operation Adjustment Model Based On An Improved Particle Swarm Optimization Algorithm

In order to ensure the normal operation of high-speed trains, the railway workers need to have unified and effective correlation of train operation adjustment plans, while arriving normally is to be an important part of operation adjustment plans. As can be seen from the mathematical model train operation adjustment and constraints for train operation adjustment are a massive multi-constraint combinatorial optimization problem. Because of the complexity of the issue, general accurate algorithm is difficult to solve, artificial intelligence methods become effective ways to solve the problem of train operation adjustments. But the widespread use of intelligent algorithms can not play a role because that they have some drawbacks. Therefore the difference between the actual train operation diagram and the scheduled diagram is minimized, the study of classic intelligent optimization genetic algorithm, the improvement of the problem become a pressing matter.PSO algorithm is actually an intelligent optimization algorithms that it likes a smart bird flying by simulating a behavior. Although the algorithm preserves the global search strategy based on population, but it reserves the speed-simple displacement model of operation, avoiding the complex genetic manipulation, so it is a more efficient parallelled searching algorithm. But as other algorithms, there are also have Shortcomings,such as,local search ability is poor, search accuracy is not high enough, the algorithm can not absolutely guarantee find the global optimal solution, it is easy to fall into the local minimum solution. But SFLA can search for and global information exchange, that can jump the local depth, balance policy that makes the algorithm jump out of local extreme points, close to the direction toward to the global optimal solution. PSO algorithm exists the problems and shortcomings, this paper Leapfrog simplifies the design of this hybrid particle swarm optimization PSO algorithm, Using this algorithm for train operation adjustment can conduct a more in-depth study, and then prove the algorithm that can effectively solve the problem of train operation adjustment.The paper starts by providing the research status and development trend of this issue at home and abroad of train operation adjustment. Combined with the actual situation, this paper puts forward a mathematical model train operation adjustment. Then this article provides an overview of the theoretical knowledge of PSO, formulas, algorithms steps, and also analyzes the knowledge of SFLA Theory. Next,For lack of the PSO algorithm, the use of the characteristics of SFLA that will apply to the PSO algorithm to ensure that the differences between the particles within each group, is to avoid the standard PSO algorithm which is easy to fall into local optimum. Finally in the same circumstance, by comparing the Leapfrog simplifies particle swarm algorithm, PSO, SFLA and improved differential algorithm, it proves the effectiveness of Leapfrog simplifies the particle swarm algorithm, showing that the algorithm provides a new way of thinking to solve the problem of the development of train operation adjustment.But the high-speed train operation adjustment work itself is a very complex and regularly work. It is very complicated because that there are new things happen. In this paper, the model and the algorithm are relatively simple, and can not handle much relatively unique work. So a lot of in-depth research need to be done that is in order to make the high-speed train running system more perfect.