一个基于PSO的节能数据收集优化算法,用于UAV任务规划
Lianhai Lin1, Zhigang Wang2, Liqin Tian1,2
1School of Computer Science, Qinghai Normal University, Xining, Qinghai, China.
PloS one
|January 19, 2024
概括
本研究引入了一种新的双自限粒子群集优化 (DSLPSO) 算法,以减少无人机数据收集系统的能源消耗. 通过提高搜索能力和动态调整参数,DSLPSO提高了无人机的运营寿命.
科学领域:
- 机器人和自动化 机器人和自动化
- 优化算法 优化算法
- 无线通信系统无线通信系统
背景情况:
- 物联网 (IoT) 时代需要有效的数据收集,无人机 (UAV) 成为一个关键技术.
- 优化无人机能源消耗对于延长任务持续时间和运营效率至关重要.
- 现有的优化算法,如遗传和群算法,在实现最佳无人机能效方面面临着挑战.
研究的目的:
- 提出一种新的优化算法,双自限粒子集群优化 (DSLPSO),专门设计以最大限度地减少无人机的能源消耗.
- 增强粒子集群优化 (PSO) 的本地和全球搜索能力,用于无人机应用.
- 通过模拟验证DSLPSO在改善无人机运营寿命方面的有效性.
主要方法:
- 开发双自限粒子群集优化 (DSLPSO) 算法,基于PSO的原则.
- 整合了两种新的机制:粒子运动限制以增强本地搜索和动态搜索范围调整以改进全球搜索.
- 实施可变人口战略和动态调整停留点,将人口视为统一的无人机任务计划.
主要成果:
- 使用公开和随机数据集的实验模拟表明了拟议的DSLPSO算法的有效性.
- DSLPSO算法显著提高了能源效率,并延长了无人机的使用寿命.
- 在DSLPSO中新引入的两个机制被证明对优化过程有积极的贡献.
结论:
- 拟议的DSLPSO算法是最大限度地降低数据收集系统中无人机能耗的有效方法.
- 限制粒子运动和动态调整搜索范围的新机制为未来的优化研究提供了重大潜力.
- DSLPSO有助于提高基于无人机的物联网应用程序的效率和寿命.
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