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Quantum Behaved Particle Swarm Optimization for origin—Destination matrix prediction

机译:用于原始的量子行为粒子群优化—目标矩阵预测

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It is difficult to obtain the satisfactory solution because traffic information is far less than the number of OD variables. This paper presents a method based on quantum-behaved particle swarm optimization (QPSO) algorithm to obtain the global optimal solution. It designs the method based on QPSO algorithm to solve the OD matrix prediction model, lists the detailed steps and points out how to choose the PSO operator. And then uses MATLAB programming language to carry out the simulation test. The simulation results show that the method has higher efficiency and accuracy.
机译:由于交通信息远远少于OD变量的数量,因此很难获得令人满意的解决方案。提出了一种基于量子行为粒子群优化算法的全局最优解。设计了基于QPSO算法求解OD矩阵预测模型的方法,列出了详细步骤,并指出了如何选择PSO算子。然后使用MATLAB编程语言进行仿真测试。仿真结果表明,该方法具有较高的效率和准确性。

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