摘要
针对动车组在不固定周转方式下的接续优化问题,首先在满足列车时刻表时空约束的条件下建立以接续时间最短为目标函数的数学模型。设计自纠正的混合粒子群算法求解该模型,为了减少算法优化复杂度,每隔T周期比较前一半T周期和后一半T周期的平均目标值,若目标值递减即按照正常粒子群速度更新公式更新,反之则按速度更新公式反向更新;得到新的种群后,按照目标值从升序排列粒子,在排序后的粒子中依次挑选两个粒子进行交叉变异操作,生成使得接续时间最短的粒子。最后分别利用自纠正混合粒子群算法(SCHPSO)、混合粒子群算法(HPSO)、粒子群算法(PSO)和遗传算法(GA)对武广线数据进行实验验证,证明了SCHPSO的有效性。
In order to solve the connection optimization problem of EMU(electric multiple units)with unfixed circulation mode,a mathematical model with the shortest connection time as the objective function is established under the condition of satisfying the space-time constraint of train schedule.A self-correcting hybrid particle swarm optimization algorithm was designed to solve the model.In order to reduce the optimization complexity of the algorithm,the average target value of the first half of the T-cycle and the last half of the t-cycle were compared every other t-cycle.If the target value decreased,it was updated according to the normal particle swarm velocity updating formula,otherwise,it was updated backwards according to the velocity updating formula.After the new population is obtained,the particles are arranged from the ascending order according to the target value,and two particles are successively selected from the sorted particles for crossover and mutation operation to generate the particle with the shortest connection time.Finally,self-correcting hybrid particle swarm optimization(SCHPSO),hybrid particle swarm optimization(HPSO),particle swarm optimization(PSO)and genetic algorithm(GA)are used to verify the data of Wuguang-Wuhan line,and the effectiveness of SCHPSO is proved.
作者
张京
张春美
ZHANG Jing;ZHANG Chun-mei(School of Electronic Information Engineering,Taiyuan University of science and Technology,Taiyuan 030024,China)
出处
《太原科技大学学报》
2024年第3期257-262,共6页
Journal of Taiyuan University of Science and Technology
基金
国家自然科学青年基金(61603266)。
