Multi-Objective Pathfinding
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 our research, we work on many-objective pathfinding. In the following papers, we propose a scalable many-objective benchmark problem covering most of the essential features for routing applications based on real-world data. We define five objective functions representing distance, travelling time, delays caused by accidents, and two route-specific features such as curvature and elevation. Since this test benchmark can be easily transferred to real-world routing problems, we construct a routing problem from OpenStreetMap data (right video). More details can be found in our papers:
- Jens Weise and Sanaz Mostaghim
- Many-Objective Pathfinding based on Fréchet Similarity Metric
- Accepted at the 11th International Conference on Evolutionary Multi-Criterion Optimization (EMO 2021), Shenzhen, China, 2021
The first video shows one of our benchmark instances with a Lake-obstacle in the middle. During the video, the search of the paths is shown. The second video shows the same process on real-world data, the Berlin map.
And we just submitted the folowing paper:
- Jens Weise and Sanaz Mostaghim
- Scalable Many-Objective Pathfinding Benchmark Suite
- Submitted to IEEE Transactions on Evolutionary Computation.--> Link