机器人技术的多代理变化方法:一种生物灵感的视角
Imran Mir1, Faiza Gul2, Suleman Mir3
1School of Avionics and Electrical Engineering, College of Aeronautical Engineering, NUST, Risalpur 23200, Pakistan.
Biomimetics (Basel, Switzerland)
|July 28, 2023
概括
本研究介绍了一种可适应的生物灵感算法,用于多代理空间探索,增强地图覆盖率并减少探索时间. 这种新的方法提高了效率,几乎没有失败的运行.
科学领域:
- 机器人技术和自主系统
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 在未知的环境中进行多代理勘探在效率和成功率方面存在挑战.
- 生物启发的算法为复杂的优化问题提供了新的方法.
研究的目的:
- 提出和评估一个可适应的,生物灵感优化算法,用于多代理空间探索 (MAE).
- 通过使用一种新的算法架构,提高太空任务的探索速度和效率.
主要方法:
- 通过将参数化的Aquila优化器与决定性的多代理探索集成,开发了多代理探索参数化Aquila优化器 (MAE-PAO).
- 在Aquila优化器中整合了随机因素以提高效率.
- 使用确定性MAE评估周围细胞的成本和效用值,然后使用参数化的Aquila Optimizer进行加速探索.
主要成果:
- 在各种环境条件下的模拟验证了MAE-PAO方法.
- 对CME-Aquila Optimizer (CME-AO) 和Whale Optimizer进行比较分析表明MAE-PAO的有效性.
- MAE-PAO实现了与当代算法相比较的勘探率和勘探时间,并且失败的运行次数明显减少.
结论:
- 拟议的MAE-PAO算法为太空探索提供了显著的优势.
- MAE-PAO提高了地图探索效率,同时减少了执行时间,并最大限度地减少了失败运行.
- 可适应的,生物灵感的方法为复杂的多代理勘探任务提供了强大的解决方案.
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