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通过改进的蜜蜂食学习粒子群群优化算法,考虑多种能源消耗的多个无人机路径规划.

Yuanhang Qi1, Haoran Jiang1,2, Gewen Huang3

  • 1School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan, 528402, China.

Scientific reports
|April 28, 2025
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概括
此摘要是机器生成的。

本研究引入了一种新的多无人飞行器路径规划模型 (MUAVPP-MEC) 和一个改进的算法 (IBFLPSO),以尽量减少飞行时间,同时考虑无线传感器网络的复杂能源消耗.

关键词:
改进了蜜蜂的食学习,优化了粒子小群的优化.粒子群集优化优化 粒子群集优化路径规划 路径规划 路径规划无人机无人机无人机是什么?

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科学领域:

  • 机器人和自动化机器人与自动化
  • 无线传感器网络 无线传感器网络
  • 优化算法 优化算法

背景情况:

  • 无人驾驶飞行器 (UAV) 在无线传感器网络中越来越多地用于数据收集.
  • 精确的能源消耗建模对于多无人机路径规划至关重要.
  • 现有的模型往往忽略了动态飞行状态,如加速和转.

研究的目的:

  • 开发一个多无人机路线规划模型 (MUAVPP-MEC),考虑各种能源消耗因素.
  • 为了最大限度地减少能源限制下的UAV总飞行时间.
  • 为解决 MUAVPP-MEC 问题提出一个高效的优化算法.

主要方法:

  • 开发了多个无人机路径规划考虑多个能源消耗 (MUAVPP-MEC) 模型.
  • 提出了一种改进的蜜蜂食学习粒子集群优化 (IBFLPSO) 算法.
  • 整合了蜜蜂食概念与粒子群优化,并采用了能源受限制的2-opt本地搜索.

主要成果:

  • MUAVPP-MEC模型准确地反映了更多的数据收集点增加的时间和能源消耗.
  • 与传统的PSO,PSO-2OPT,GA和BFLPSO相比,IBFLPSO算法显示出更高的性能.
  • IBFLPSO实现了明显更好的最佳解决方案,高达54.64%的表现优于其他.

结论:

  • 拟议的MUAVPP-MEC模型和IBFLPSO算法对于能源意识的多UAV路径规划是有效的.
  • IBFLPSO为无人机网络中复杂的优化问题提供了强大而高效的解决方案.
  • 这些发现强调了全面能源建模对于优化无人机运营的重要性.