Scalable Many-Objective Pathfinding Benchmark Suite Pareto-fronts and Sets

Citation Author(s):
Jens
Weise
Otto von Guericke University Magdeburg
Sanaz
Mostaghim
Otto von Guericke University Magdeburg
Submitted by:
Jens Weise
Last updated:
Wed, 10/28/2020 - 08:40
DOI:
10.21227/ff8m-fr60
Data Format:
License:
0
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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. This dataset provides the obtained reference fronts from the exhaustive search on the benchmark instances.

Instructions: 

In folder 'referenceFronts', you can find the corresponding Pareto-Fronts (.pf) (comma seperated values) and -Sets (.ps)Deserilisation of the sets is possible through Gson/JSON. Each line contains all nodes of Path, delimited by '||'

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