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首页> 外文期刊>International Journal of Artificial Intelligence Tools: Architectures, Languages, Algorithms >Computing Multicriteria Shortest Paths in Stochastic Multimodal Networks Using a Memetic Algorithm
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Computing Multicriteria Shortest Paths in Stochastic Multimodal Networks Using a Memetic Algorithm

机译：使用迭代算法计算随机多模态网络中的多轨道最短路径

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摘要

The human mobility is nowadays always organized in a multimodal context. However, the transport system has become more complex. Consequently, for the sake of helping passengers, building Advanced Travelers Information Systems (ATIS) has become a certain need. Since passengers tend to consider several other criteria than the travel time, an efficient routing system should incorporate a multi-objective analysis. Besides, the transport system may behave in an uncertain manner. Integrating uncertainty into routing algorithms may thus provide more robust itineraries. The main objective of this paper is to propose a Memetic Algorithm (MA) in which a Genetic Algorithm (GA) is combined with a Hill Climbing (HC) local search procedure in order to solve the multicriteria shortest path problem in stochastic multimodal networks. As transport modes, railway, bus, tram and metro are considered. As optimization criteria, stochastic travel time, travel cost, number of transfers and walking time are taken into account. Experimental results have been assessed by solving real life itinerary problems defined on the transport network of the city of Paris and its suburbs. Results indicate that unlike classical deterministic algorithms and pure GA and HC, the proposed MA is efficient enough to be integrated within real world journey-planning systems.
机译：现在，人类流动性始终在多模式背景下组织。然而，运输系统变得更加复杂。因此，为了帮助乘客，建立先进的旅行者信息系统（ATIS）已成为一定需求。由于乘客倾向于考虑比旅行时间的几个其他标准，因此有效的路由系统应包含多目标分析。此外，运输系统可能以不确定的方式行事。因此，将不确定性集成到路由算法中可以提供更强大的行程。本文的主要目的是提出一种遗漏算法（MA），其中遗传算法（GA）与山坡（HC）本地搜索程序组合，以便解决随机多模式网络中的多轨机最短路径问题。作为运输模式，考虑到铁路，公共汽车，电车和地铁。作为优化标准，随机旅行时间，旅行成本，转移数量和步行时间被考虑在内。通过解决在巴黎市及其郊区的运输网络上定义的现实生活行程问题评估了实验结果。结果表明，与经典的确定性算法和纯GA和HC不同，所提出的MA有效地集成在真实世界的行程规划系统内。

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