Traditional route planners commonly focus on finding the shortest path between two points in terms of travel distance or time over road networks. However, in real cases, especially in the era of smart cities where many kinds of transportation-related data become easily available, recent years have witnessed an increasing demand of route planners that need to optimize for multiple criteria, e.g., finding the route with the highest accumulated scenic score along (utility) while not exceeding the given travel time budget (cost). Such problem can be viewed as a variant of arc orienteering problem (AOP), which is well-known as an NP-hard problem. In this paper, targeting a more realistic AOP, we allow both scenic score (utility) and travel time (cost) values on each arc of the road network are time-dependent (2TD-AOP), and propose a memetic algorithm to solve it. To be more specific, within the given travel time budget, in the phase of initiation, for each population, we iteratively add suitable arcs with high scenic score and build a path fromthe origin to the destination via a complicate procedure consisting of search region narrowing, chromosome encoding and decoding. In the phase of the local search, each path is improved via chromosome selection, local-improvement-based mutation and crossoveroperations. Finally, we evaluate the proposed memetic algorithm in both synthetic and real-life datasets extensively, and the experimental results demonstrate that it outperforms the baselines.

中文翻译：

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立即享受最美丽的场景：一种模因算法，可解决与时间有关的两倍电弧定向运动问题

传统的路线规划师通常着眼于在道路网络上的行驶距离或时间上找到两点之间的最短路径。但是，在实际情况下，尤其是在智能城市时代，可以轻松获得许多与交通相关的数据，近年来，路线规划师的需求不断增长，需要针对多种标准进行优化，例如，通过沿（公用事业）的最高累计景区得分，但不超过给定的旅行时间预算（费用））。可以将这种问题视为电弧定向运动问题（AOP）的一种变体，它是众所周知的NP硬问题。在本文中，针对更现实的AOP，我们允许路网的每条弧线上的景区得分（效用）和旅行时间（成本）值都是时间相关的（2TD-AOP），并提出了一种模因算法来解决。更具体地说，在给定的旅行时间预算内，在初始阶段，针对每个人群，我们反复添加具有较高风景得分的合适弧线，并通过复杂的过程（从缩小搜索范围，染色体编码和解码。在本地搜索阶段，通过染色体选择，基于局部改进的突变和交叉操作。最后，我们在合成数据集和实际数据集中广泛评估了拟议的模因算法，实验结果表明该算法优于基线。