Benchmarking global optimization techniques for unmanned aerial vehicle path planning
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Date
2025-12-01
Authors
Shehadeh, Mhd Ali
Kůdela, Jakub
0000-0001-5550-4650Advisor
Referee
Mark
Journal Title
Journal ISSN
Volume Title
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
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Abstract
The Unmanned Aerial Vehicle (UAV) path planning problem is a complex optimization problem in the field of robotics. In this paper, we investigate the possible utilization of this problem in benchmarking global optimization methods. We devise a problem instance generator and pick 56 representative instances, which we compare to established benchmarking suits through Exploratory Landscape Analysis to show their uniqueness. For the computational comparison, we select fourteen well-performing global optimization techniques from both subfields of stochastic algorithms (evolutionary computation methods) and deterministic algorithms (Dividing RECTangles, or DIRECT-type methods). The experiments were conducted in settings with varying dimensionality and computational budgets. The results were analyzed through several criteria (number of best-found solutions, mean relative error, Friedman ranks) and utilized established statistical tests. The best-ranking methods for the UAV problems were almost universally the top-performing evolutionary techniques from recent competitions on numerical optimization at the Institute of Electrical and Electronics Engineers Congress on Evolutionary Computation. Lastly, we discussed the variable dimension characteristics of the studied UAV problems that remain still largely under-investigated. The code and results are available at a Zenodo repository https://doi.org/10.5281/zenodo.15424080.
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Citation
EXPERT SYSTEMS WITH APPLICATIONS. 2025, vol. 293, issue Dec, p. 1-19.
https://www.sciencedirect.com/science/article/pii/S095741742502264X
https://www.sciencedirect.com/science/article/pii/S095741742502264X
Document type
Peer-reviewed
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Published version
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en