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.

I. Introduction

Optimal pathfinding is among the most challenging tasks for industrial and logistical applications [1]. Any improvement in the quality of results can have a considerable impact on many factors, such as fuel consumption and the environment. The current state-of-the-art path planning algorithms usually consider the travel time and the distance in the optimization. However, specific applications encounter additional criteria, such as the curvature of the route, the elevation (ascent), or environmental issues such as air pollution caused by fuel consumption. These criteria can profoundly influence the practicability of the solutions. For instance, for animal transportation, we need to additionally minimize the number of curves in the route (or maximize the smoothness). Reducing the length of the path can help to reduce fuel consumption while possibly increasing the traveling time. Other criteria such as the ascent of a path can be considered for heavy vehicles which can consume more fuel on such nonflat routes.