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Volume 18, Issue 1
  • ISSN: 2666-2558
  • E-ISSN: 2666-2566
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Abstract

Background

With the development of intelligent technology, Unmanned aerial vehicles (UAVs) are widely used in military and civilian fields. Path planning is the most important part of UAV navigation system. Its purpose is to find a smooth and feasible path from the start to the end.

Objective

In order to obtain a better flight path, this paper presents an improved Aquila optimizer combing the opposition-based learning and the local escaping operator, named LEOAO, to deal with the UAV path planning problem in three-dimensional environments.

Methods

UAV path planning is modelled as a constrained optimization problem in which the cost function consists of one objective: path length and four constraints: safe distance, flight height, turning angle and climbing/diving angle. In this paper, the LEOAO is introduced to find the optimal path by minimizing the cost function, and B-Spline is invited to represent a smooth path. The local escaping operator is used to enhance the search ability of the algorithm.

Results

To test the performance of LEOAO, two scenarios are applied based on basic terrain function. Experiments show that the proposed LEOAO outperforms other algorithms such as the grey wolf optimizer, whale optimization algorithm, including the original Aquila optimizer.

Conclusion

The proposed algorithm combines the opposition-based learning and local escaping operator. The opposition-based learning algorithm has the ability to accelerate convergence. And the introduction of LEO effectively balances the exploration and exploitation abilities of the algorithm and improves the quality of the population. Finally, the improved Aquila optimizer obtains a better path.

© 2025 Bentham Science Publishers
© 2025 The Author(s). Published by Bentham Science Publisher. This is an open access article published under CC BY 4.0 https://creativecommons.org/licenses/by/4.0/legalcode
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2024-04-25
2025-01-15

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An Improved Aquila Optimizer with Local Escaping Operator and Its Application in UAV Path Planning
Recent Advances in Computer Science and Communications 18, E250424229337 (2025); https://doi.org/10.2174/0126662558295501240418093550
/content/journals/rascs/10.2174/0126662558295501240418093550
/content/journals/rascs/10.2174/0126662558295501240418093550
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  • Article Type:     Research Article
Keyword(s): aquila optimizer; constrained optimization; local escaping operator; opposition-based learning; swarm intelligence; UAV path planning

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