Abstract: The classic genetic algorithm has been used for the optimization of train operation long ago. However, due to the uncertainty of population evolution direction and insufficient local search ability, the rate of convergence is slow and the quality of solution is low. In this paper, an improved genetic algorithm is proposed to study the optimization of train operation profile. The optimization objective is to minimize the energy consumption of train operation. The constraints are transformed into penalty functions, such as traffic safety, punctuality and precise parking etc. In order to accelerate the population convergent rate and improve the solution quality, a new mechanism is designed, which can guide the evolution direction of the population, and the punctuality adjustment and local search are included in the new mechanism. The demonstrations show that the improved genetic algorithm is suitable for train operation profile optimization and can improve the convergence speed effectively. Moreover, it’s result is more energy saving than the classic genetic algorithm and the adaptive genetic algorithm.

Key words: train energy saving optimization, improved genetic algorithm, evolutionary direction guidance mechanism, punctuality adjustment, local search