1. Hybrid ant colony and intelligent water drop algorithm for route planning of unmanned aerial vehicles.
- Author
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Sun, Xixia, Pan, Su, Bao, Nan, and Liu, Ning
- Subjects
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ANT algorithms , *ANT colonies , *DRONE aircraft , *SEARCH engines , *ALGORITHMS , *MATHEMATICAL models - Abstract
• A mathematical model of the UAV route planning problem is established. • A hybrid ACO-IWD algorithm, which combines the advantages of the ACO and IWD algorithms, is proposed to solve the UAV route planning problem. • A novel node selection strategy is designed to guide the attempts of the agents to search for routes in a reasonable direction and increase their search efficiency. • The advantages of the proposed algorithm over the state-of-the-art algorithms are demonstrated by extensive experimental results. Route planning is a crucial element in unmanned aerial vehicle (UAV) systems, particularly in autonomous UAV technology. In the past decades, various algorithms have been proposed for UAV route planning. However, they still have defects, such as stagnation and slow search rates. In this study, a novel hybrid algorithm which integrates ant colony optimization (ACO) and intelligent water drop (IWD) is proposed for UAV route planning. First, the advantages of the IWD and ACO algorithms are combined in an iterative strategy, to ensure mutual cooperation via exchange of information for route optimization. Initially, the water drops optimize the soil and pheromones within the environment simultaneously to generate good approximate solutions and an initial pheromone distribution for the ant colony. Based thereupon, the ant colony roams the solution space to further optimize the routes, thus combining the exploration potential and exploitability of two types of agents. Additionally, a novel node selection strategy is proposed to guide the agents' route planning along a reasonable direction. Compared with state-of-the-art algorithms, the convergence accuracy, success rate, and stability of the proposed algorithm exhibited significant improvements of approximately 8.25%, 4.20%, and 66.20%, respectively. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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