Patent ID: 11875271
Assignee: XIANGTAN UNIVERSITY
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 4:
5. A system for train periodic message scheduling based on a multi-objective evolutionary algorithm, comprising:
an MVB periodic message table acquiring module, adapted to acquire an MVB periodic message table, where the MVB periodic message table includes K periodic messages to be transmitted; each of the periodic messages has a period, a processing time and a priority; and the MVB periodic message table corresponds to a macro cycle containing N micro cycles;
an iterative population generating module, adapted to binary encode the MVB periodic message table and initialize it randomly, to generate an iterative population, where the iterative population includes M individuals; each of the individuals includes K binary codes; each of the binary codes is obtained from: binary encoding a sequential number of the micro cycle in which a periodic message is scheduled for the first time, and then initializing it randomly; and different binary codes correspond to different periodic messages;
a crossover and mutation module, adapted to perform crossover and mutation operations on the individuals of the iterative population using a genetic algorithm, to update the iterative population, the number of individuals in the updated iterative population being 2M;
a fitness value calculating module, adapted to calculate a fitness value for each individual in the updated iterative population according to a constructed multi-objective function, and record the current number of iterations, where the multi-objective function includes a first objective calculation function and a second objective calculation function; the fitness value is the sum of a first objective and a second objective; the first objective is the sum of sequential numbers of all the micro cycles in which the periodic messages are scheduled for the first time respectively; the second objective is the sum of the processing times for all the periodic messages;
a determining module, adapted to determine whether the current number of iterations is less than a preset total number of iterations, to obtain a first determination result;
a next-generation iterative population generating module, adapted to, when the first determination result indicates that the current number of iterations is less than the preset total number of iterations, add 1 to the current number of iterations, perform dominated selection and sequencing on the individuals of the updated iterative population according to the fitness value of each individual, and select the top M individuals to form a next-generation iterative population and return it to the crossover and mutation module;
an MVB periodic scheduling table constructing module, adapted to, when the first determination result indicates that the current number of iterations is not less than the preset total number of iterations, construct an MVB periodic scheduling table according to the individuals with the smallest fitness values;
a scheduling module, adapted to schedule periodic messages according to the MVB periodic scheduling table.