多目标量子混合进化算法,用于提高物联网服务质量
Shailendra Pratap Singh1, Gyanendra Kumar2, Umakant Ahirwar3
1Department of Computer Science and Engineering, Madan Mohan Malaviya University of Technology Gorakhpur-273010 (U.P.), Gorakhpur, UP, India.
Scientific reports
|April 28, 2025
概括
本研究介绍了一种量子启发的混合算法,以优化物联网 (IoT) 服务质量 (QoS). 这种新的方法提高了能源效率,并减少了物联网应用中的延迟.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 网络工程 网络工程
背景情况:
- 在物联网 (IoT) 中优化服务质量 (QoS) 是由于设备异质性和资源限制而具有挑战性的.
- 传统的多目标优化算法在复杂的物联网环境中难以实现缓慢的融合和局部优化.
研究的目的:
- 为有效的物联网服务管理提出一种新的量子启发的混合优化算法.
- 提高物联网应用中的QoS参数,包括能源效率,延迟,融合速度和覆盖成本.
主要方法:
- 开发了一个混合算法,集成多目标灰狼优化算法 (MOGWOA) 和多目标鱼优化算法 (MOWOA).
- 纳入量子原理,如量子位置和行为,以提高勘探和开发能力.
- 进行了广泛的模拟,以评估算法的性能与现有方法相比.
主要成果:
- 拟议的量子启发的混合算法证明了改进的融合速度和避免局部最佳.
- 在物联网应用中实现了卓越的优化结果,以提高能源效率和减少延迟.
- 与传统算法相比,在融合和覆盖成本方面验证了增强的性能.
结论:
- 这种新的量子启发的混合算法有效地解决了物联网 QoS 优化传统方法的局限性.
- 量子力学的集成显著提高了对复杂物联网挑战的算法的效率和准确性.
- 拟议的方法为改善整体物联网服务管理和性能提供了一个有希望的解决方案.
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