Abstract
The goal of 3D UAV path planning problem is to assist the UAV in planning a flight path with the lowest total overhead cost. In this paper, we present a novel approach to address the problem by incorporating flight distance, threat cost, flight altitude and path smoothness constraints into a comprehensive cost function. The current popular metaheuristic algorithm is utilized to solve for the closest globally optimal UAV flight path. To overcome the challenges of local optima and slow convergence associated with the conventional Beluga Whale Optimizer (BWO), this paper proposes a modified beluga whale optimizer (OGGBWO) based on random opposition-based learning strategy, adaptive Gauss variational operator and elitist group genetic strategy. Extensive experiments conducted on the CEC2022 test set and four distinct terrain scenarios of varying complexity demonstrate that the OGGBWO algorithm outperforms classical and state-of-the-art metaheuristics. It achieves superior optimization performance across all 12 CEC2022 test functions and exhibits exceptional convergence in generating flight paths with the lowest total cost function in diverse terrain scenarios.
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This research is supported by the National Natural Science Foundation of China (No. 71863018 and No. 71403112), Jiangxi Provincial Social Science Planning Project (No. 21GL12) and Technology Plan Projects of Jiangxi Provincial Education Department (No. GJJ200424).
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Ye, C., Wang, W., Zhang, S., Shao, P. (2024). Optimizing 3D UAV Path Planning: A Multi-strategy Enhanced Beluga Whale Optimizer. In: Luo, B., Cheng, L., Wu, ZG., Li, H., Li, C. (eds) Neural Information Processing. ICONIP 2023. Lecture Notes in Computer Science, vol 14448. Springer, Singapore. https://doi.org/10.1007/978-981-99-8082-6_4
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DOI: https://doi.org/10.1007/978-981-99-8082-6_4
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