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一种创新的覆盖率优化方法,用于在农业物联网中智能信息监控,使用多策略的Pelican优化算法

Wei Chen¹, Qike Cao², Bingyu Cao³

¹School of Information Science and Engineering, Xinjiang College of Science & Technology, Korla, 841000, Xinjiang, China.

Scientific reports

|April 12, 2025

概括

一个新的多策略优化算法 (MSPOA) 增强了农业无线传感器网络 (AWSN) 覆盖范围,以实现智能农业. 这种方法提高了作物监测效率和大规模田地的资源使用.

科学领域:

农业工程农业工程
计算机科学计算机科学
优化算法优化算法

背景情况:

智能农业依赖于农业无线传感器网络 (AWSN) 进行作物监测.
优化AWSN覆盖面对效率至关重要,但在大规模部署中受到NP难题的挑战.
传统的算法在传感器网络优化的准确性和局部收方面存在局限性.

研究的目的:

引入一种新的优化算法,以提高AWSN在大型农业领域的覆盖率.
解决现有算法在准确性和趋同性方面的局限性.
通过优化传感器网络,提高智能农业的生产效率和资源利用.

主要方法:

提出了一个多策略客优化算法 (MSPOA).
综合策略:良好的点设置,3D螺旋莱维飞行和自适应的T分布变化.
整合了受灵感的运动和协作,以提高适应性和强度.

主要成果:

与IABC,CAFA,APSO和LCSO相比,MSPOA在网络覆盖率方面取得了显著的改善.
与基准算法相比,实现了5.85%,11.33%,21.05%和20.66%的覆盖率增加.
在动态农业环境中,MSPOA表现出强大的适应性和稳定性.

结论:

新的MSPOA有效地优化了AWSN对智能农业的覆盖范围.
MSPOA克服了传统算法的局限性,提供了更高的准确性和全球搜索功能.
该算法的适应性和稳定性对现实世界农业应用有好处.

关键词:

适应分布的t-变化算法建模和模拟的算法建模和模拟.征收飞行战略征收飞行战略佩利坎优化算法的优化算法定点策略定点策略是一个策略.无线传感器网络无线传感器网络

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