CQLHBA:节点覆盖率优化使用混乱的量子灵感的领导者蜂蜜子算法
Xiaoliu Yang1, Mengjian Zhang2
1Department of Automation Engineering, Moutai Institute, Renhuai 564507, China.
Biomimetics (Basel, Switzerland)
|December 24, 2025
概括
一个新的混沌量子启发的领导者蜂蜜子算法 (CQLHBA) 提高了节点覆盖优化 (NCO) 的准确性. 这个群集智能算法为无线传感器网络提供了增强的全球搜索和稳定性.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 优化算法 优化算法
背景情况:
- 对于节点覆盖优化 (NCO) 的现有群集智能 (SI) 算法,其解决方案准确性有限.
- 节点覆盖率优化对于无线传感器网络的高效部署至关重要.
研究的目的:
- 提出一种新的混沌量子启发的领导者蜂蜜子算法 (CQLHBA),以解决NCO的精度限制.
- 增强群众智能算法的探索和利用能力.
主要方法:
- 通过将混乱动力学,量子旋转和莱维飞行策略与基本的蜂蜜子算法 (HBA) 集成,开发了CQLHBA.
- 实施了参数α1的调整策略,以平衡追随者位置优化和改进探索.
- 使用21个基准函数验证性能,并与最先进的SI算法进行比较.
主要成果:
- 与其他SI算法相比,CQLHBA在基准函数上的表现优越.
- 该算法展示了增强的全球搜索能力和强大的稳定性.
- 应用于无线传感器网络 (WSN) 的NCO,CQLHBA在覆盖率和网络连接性方面取得了显著的改进.
结论:
- 拟议的CQLHBA对NCO的现有SI算法提供了显著的进步.
- 新战略的整合有效地提高了勘探和开采.
- CQLHBA显示了WSN的实际有效性和现实应用的潜力.
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