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Due to the significant interest in Unmanned Aerial VehiclesUnmanned aerial vehicle (UAVs), numerous metaheuristicMetaheuristic or nature-inspired algorithmsAlgorithms have been proposed for path-planning problems, allowing units to navigate through diverse threats effectively. By strategically avoiding threats identified by radar, these algorithmsAlgorithms aim to determine the most optimal route. This chapter explores the application of two variants of the Bees AlgorithmBees algorithm (BA) to address this issue and evaluates their performance against other metaheuristic algorithmsMetaheuristic algorithm. The comparison encompasses various configurations of waypoints and threats, with statistical analysesStatistical analysis conducted to assess performance differences. The findings indicate that the Bees AlgorithmBees algorithm with two parameters (BA2) surpasses the performance of the Basic BA and other metaheuristic algorithmsMetaheuristic algorithm. Statistical estimates derived from the data are utilised for comparisons, ensuring that optimisationOptimisation parameters for all algorithmsAlgorithms are selected unbiased and meaningfully.

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Utilising the Bees Algorithm for UAV Path Planning—A Simultaneous Collision Avoidance and Shortest Path Approach

  • 章节

  • 发表日期：

    2025年

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  • Anubhab Dasgupta,
  • Satyam Kumar,
  • Aaditri Vaibhav,
  • Asrul Harun Ismail

摘要

Due to the significant interest in Unmanned Aerial VehiclesUnmanned aerial vehicle (UAVs), numerous metaheuristicMetaheuristic or nature-inspired algorithmsAlgorithms have been proposed for path-planning problems, allowing units to navigate through diverse threats effectively. By strategically avoiding threats identified by radar, these algorithmsAlgorithms aim to determine the most optimal route. This chapter explores the application of two variants of the Bees AlgorithmBees algorithm (BA) to address this issue and evaluates their performance against other metaheuristic algorithmsMetaheuristic algorithm. The comparison encompasses various configurations of waypoints and threats, with statistical analysesStatistical analysis conducted to assess performance differences. The findings indicate that the Bees AlgorithmBees algorithm with two parameters (BA2) surpasses the performance of the Basic BA and other metaheuristic algorithmsMetaheuristic algorithm. Statistical estimates derived from the data are utilised for comparisons, ensuring that optimisationOptimisation parameters for all algorithmsAlgorithms are selected unbiased and meaningfully.