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Wheel-rail adhesion often reaches its limit states when heavy-haul and high-speed trains traveling. In order to obtain the maximum adhesive force utilization, this paper put forward a dynamic multiple sub-population QPSO algorithm to evolve a neural network, and designed the intelligent optimization controller based on the network to implement the optimized adhesion control of locomotive. By dynamically adjusting the motor torque, it achieved the optimal wheel-rail adhesion force. In simulation study, this paper used a typical test function to test the performance of the dynamic multiple sub-population QPSO algorithm. The simulation results demonstrate the relatively high accuracy and efficiency of the algorithm, and prove that the algorithm can improve the convergence speed and learning ability of the neural network, at the same time, in optimized adhesion control of locomotive, the intelligent optimization controller can also get a good control effect. [ABSTRACT FROM AUTHOR]