基于混合粒子群算法的列车停站方案优化
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Optimization of Train Stopping Scheme Based on Hybrid Particle Swarm Algorithm
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    摘要:

    列车停站方案影响着旅客服务质量和运行效率,是列车开行方案的重要环节.本文建立了旅客列车停站方案的多目标规划模型以最大化区段可达性从而减少旅客旅行时间.针对传统的粒子群优化算法在处理复杂多维问题时,算法效率不高,易陷进局部最优,且无法有效处理离散问题等缺点,提出了一种将量子遗传算法引入到MPSO中的方法.算法整体采用粒子群算法,结合量子遗传算法的概率幅编码,并使用粒子群的速度更新公式来更新量子旋转门.算法引入量子遗传算法的全局探索和粒子群算法的种群智能体系,不仅提高了算法的收敛速度,同时增加了粒子多样性.最后,将改进的量子遗传粒子群算法(QGA_PSO)应用于ZDT函数优化和停站方案模型优化,证明了算法的有效性.

    Abstract:

    The stopping scheme for passenger trains is important to the operation planning of trains, and the scheme affects the quality of passenger service and the transportation efficiency. This study established a multi-target programming model, aiming to minimize the total travel time of passengers and maximize the zone accessibility. In view of the traditional Particle Swarm Optimization (PSO) algorithm, which is inefficient and easy to fall into local optimum and cannot effectively handle the discrete problems when dealing with complex high dimensional problems, a new hybrid particle swarm algorithm is proposed based on the Quantum Genetic Algorithm (QGA). First, the algorithm adopted the construction of particle swarm algorithm, employing the idea of quantum bit coding, and using PSO algorithm velocity update mechanism to update the quantum revolving door. Since the algorithm combined the global exploration of QGA and intelligent system PSO populations, which not only improves the convergence speed of algorithm, but also increases the diversity of particle. Finally, the experiment on the ZDT function optimum and stopping scheme optimum problem shows that the proposed algorithm consistently provides faster convergence and precision.

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陈晓敏,王家伟.基于混合粒子群算法的列车停站方案优化.计算机系统应用,2018,27(6):12-17

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  • 收稿日期:2017-10-09
  • 最后修改日期:2017-11-01
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  • 在线发布日期: 2018-05-29
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