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Quantum-inspired Evolutionary Algorithm for Transportation Network Design Optimization

机译:运输网络设计优化量子启发进化算法

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Transportation network design problem deals with how to add or improve some edges on an existing transportation network to improve traffic condition. In this study a bi-level programming model was proposed to optimize the strategy of transportation network capacity improvement in the constraint of budget. The upper level problem aims to minimize the total travel time of all transportation travelers, while the lower level model is users' equilibrium transportation assignment model. A quantum-inspired evolutionary algorithm was employed to solve the problem. The result of numerical experiment indicated that the proposed model can reduce total travel time by searching optimal solution and the QEA is more efficient than other heuristic algorithm.
机译:运输网络设计问题处理如何在现有的运输网络上添加或改进一些边缘以改善交通状况。在本研究中,提出了一种双级编程模型,以优化预算​​限制的运输网络能力改善策略。上层问题旨在最大限度地减少所有运输旅行者的总旅行时间,而下级模型是用户的平衡运输分配模型。采用量子启发的进化算法来解决问题。数值实验的结果表明,所提出的模型可以通过搜索最佳解决方案来减少总旅行时间,并且QEA比其他启发式算法更有效。

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