一种新的群集智能优化方法,用于在工业无线传感器网络中有效分配任务
1College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin, 132000, China.
Scientific reports
|October 10, 2025
概括
一个新的混沌精英克隆粒子群集优化 (CECPSO) 算法通过有效分配任务来提高工业无线传感器网络 (IWSN) 的性能. 在融合和整体效率方面,CECPSO的表现优于现有方法.
科学领域:
- 计算机科学 计算机科学
- 电气工程 电气工程
- 优化算法 优化算法
背景情况:
- 工业无线传感器网络 (IWSNs) 在大规模部署中面临由于通信,计算和能源限制的性能限制.
- 有效的任务分配对于提高IWSN性能至关重要.
- 在IWSN中任务分配是一个NP难题,随着网络和任务大小的增加而增加复杂性.
研究的目的:
- 为IWSNs提出一个高效的任务分配算法.
- 为了解决IWSNs中的任务分配的NP-hard性质.
- 通过优化任务分配,提高IWSNs的整体性能.
主要方法:
- 介绍混沌理论以优化最初的人口.
- 精英克隆策略的设计,以加快解决方案的太空探索和提高准确性.
- 实施动态调整策略,以避免早期的局部优化.
- 采用指数式非线性下降惯性权重函数用于均衡的本地和全球搜索.
主要成果:
- 拟议的混沌精英克隆粒子集群优化 (CECPSO) 算法与粒子集群优化 (PSO),遗传算法 (GA) 和模拟化 (SA) 相比表现出更高的性能.
- 在各种实验场景中,CECPSO表现出更高的收率和更好的整体表现.
- 在40个传感器和240个任务的条件下,CECPSO比PSO提高了6.6%,比GA提高了21.23%,比SA提高了17.01%.
结论:
- CECPSO算法有效地提高了工业无线传感器网络的整体性能.
- 拟议的方法为IWSNs中复杂的任务分配问题提供了可靠的解决方案.
- 对于IWSN任务分配,CECPSO在趋同率和解决方案准确性方面提供了显著的改进.
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