Abstract
This study proposed a new genetic algorithm with variable neighbourhood search (GAVNS) for UAV path planning in three-dimensional space. First, an 0–1 integer programming mathematical model is established by inspired from the vehicle routing planning model with time window (VRPTW), and then a heuristic rule based on space vector projection is designed to quickly initialize high-quality solutions that meet constraints of upper error limit and minimum turning radius. Second, it improves mutation operator with a reselected mutation strategy, and incorporates Variable Neighborhood Search strategy based on adding and deleting route during the search process; Finally, GAVNS is compared with general Genetic Algorithm on a set of experiments. It is demonstrated that GAVNS algorithm is both effective and efficient. Moreover, the introduction of variable neighborhood search strategy enhances the local search ability of Genetic Algorithm.
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Acknowledgement
This work is supported in part by the National Nature Science Foundation of China under Grant 61773390 and Grant 61973310, the key project of National University of Defense Technology (ZK18–02–09), the Hunan Youth elite program (2018RS3081) and the key project ZZKY–ZX–11–04.
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Zhang, G., Wang, R., Lei, H., Zhang, T., Li, W., Song, Y. (2021). UAV Path Planning Based on Variable Neighborhood Search Genetic Algorithm. In: Tan, Y., Shi, Y. (eds) Advances in Swarm Intelligence. ICSI 2021. Lecture Notes in Computer Science(), vol 12690. Springer, Cham. https://doi.org/10.1007/978-3-030-78811-7_20
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DOI: https://doi.org/10.1007/978-3-030-78811-7_20
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