A Scalable Many-Objective Pathfinding Benchmark Suite

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. Most route planning algorithms consider only up to three objectives. In this article, 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 analyze several different instances for this test problem and provide their true Pareto front to analyze the problem difficulties. Additionally, we apply four well-known evolutionary multiobjective 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 optimization 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 optimization problem enabling researchers and engineers to evaluate their many-objective approaches.