Application of multi-dimension parallel genetic algorithm to energy-saving optimum control of trains in following operation
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Abstract:
In order to study the energy-saving optimum control strategy of a leading train and a tracing train in following operation under a moving block system，an energy-saving control model of trains is created. The aims of the model are energy consumption and trip time error. The control variables of this model are the operating handle level and the train’s position when the operating handle level is changed. Based on the model，the static and dynamic speed restraints are put forward. The static speed restraints are defined by the line conditions and the dynamic speed restraints of the tracing train caused by the leading train for the sake of safety. This problem is solved with the help of multi-dimension parallel genetic algorithm (GA) and external punishment function. During the solving process，the crossover probability and the mutation probability are adjusted dynamically according to the GA generation to improve the efficiency of the coarse grain search and the fine grain search. Ramps divided into three parts and the real number coding are adopted to shorten the length of chromosomes and improve the speed of convergence. Its correctness and effectiveness are validated at a simulation platform of train operation.
