Scalable Many-Objective Pathfinding Benchmark Suite

TL;DR

This paper introduces a scalable many-objective route planning benchmark based on real-world data, enabling evaluation of algorithms in complex, multi-criteria pathfinding scenarios relevant to logistics and robotics.

Contribution

It presents a new benchmark suite for many-objective pathfinding, including real-world data and multiple conflicting objectives, facilitating research in scalable multi-criteria routing algorithms.

Findings

Three algorithms showed promising results on the benchmark.

The benchmark can be applied to real-world routing problems.

True Pareto-fronts were analyzed for different instances.

Abstract

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.