چكيده به لاتين
Train speed profile is one of the most fundamental optimization issues in rail transportation. The speed profile has several indices, such as time travel and energy consumption. These indices mostly have opposite natures, which leads to a non-linear, complex multi-objective optimization problem. This paper introduces a new heuristic algorithm called Conscious Search (CS) to determine the optimal speed profile. For this purpose, after modeling the train dynamics, we initially analyze the validated algorithms, including Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Bees Algorithm (BA), and Teaching-learning Based Optimization (TLBO) statistically using sensitivity analysis. Then, using new criteria called the Impact Factor, we show that a trade-off prevails in the global and local search for algorithms. In general, the stronger the algorithm in one domain, the weaker in the other. Subsequently, no algorithm is absolutely superior to another. We propose CS as a hybrid algorithm that performs the optimization in two separated global and local phases to address this problem and perform the ideal optimization. CS steps are designed to dominate other algorithms in both local and global phases. In the evaluation, we obtained the train speed profile using the proposed method and the mentioned algorithms. Based on the simulation results, CS outperforms the considered algorithms by a palpable margin and its solutions dominate others. These results indicate on effectiveness and excellence of the proposed method.
Moreover, in this research, the problem of speed tracing for automatic train operation is studied. A new Intelligent-PID controller is proposed in which four optimization algorithms: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and Imperium Colony Algorithm (ICA) for the best parameter tuning with the integration of a switch function are used. The algorithms are analyzed and specialized for different driving modes including: acceleration, cruising, braking and speed profile shift. By the use of a switch, the PID controller is tuned according to the best algorithm. The switching action is done through a slight change from the current position to the best values by transient values determined by the other algorithm outputs. The simulation results indicate the excellence of the proposed method. The performance of the suggested structure is compared with a single-mode optimization algorithm without sue of the switch. The results of the comparison show that the proposed method can track the trajectory on all driving modes with very high accuracy
