可扩展的多目标寻路基准套件
Scalable Many-Objective Pathfinding Benchmark Suite
摘要：
路径规划也被称为寻路，是物流、移动机器人和其他应用中的关键要素之一，工程师们面临着许多相互冲突的目标。然而，目前大多数的路径规划算法只考虑多达三个目标。在本文中，我们提出了一个可扩展的多目标基准问题，涵盖了大多数的重要功能的路由应用程序的基础上，现实世界的数据。我们定义了五个目标函数，代表距离，行驶时间，事故造成的延误，和两个路线的具体功能，如曲率和海拔。我们分析了几个不同的情况下，这个测试问题，并提供其真正的帕累托前沿分析问题的困难。我们采用三个著名的进化多目标算法。由于这个测试基准可以很容易地转移到现实世界的路由问题，我们构建了一个路由问题，从OpenStreetMap数据。我们评估的三个优化算法，并观察到，我们能够提供这样一个现实世界的应用前景看好的结果。建议的基准代表了一个可扩展的多目标的路线规划优化问题，使研究人员和工程师评估他们的多目标的方法。
英文摘要：
Route planning also known as pathfinding is one of the key elements in logistics, mobile robotics and other applications, where engineers face many conflicting objectives. However, most of the current route planning algorithms consider only up to three objectives. In this paper, we propose a scalable many-objective benchmark problem covering most of the important features for routing applications based on real-world data. We define five objective functions representing distance, traveling time, delays caused by accidents, and two route specific features such as curvature and elevation. We analyse several different instances for this test problem and provide their true Pareto-front to analyse the problem difficulties. We apply three well-known evolutionary multi-objective algorithms. Since this test benchmark can be easily transferred to real-world routing problems, we construct a routing problem from OpenStreetMap data. We evaluate the three optimisation algorithms and observe that we are able to provide promising results for such a real-world application. The proposed benchmark represents a scalable many-objective route planning optimisation problem enabling researchers and engineers to evaluate their many-objective approaches.
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