用蜜蜂觅食启发算法，智能布置传感器以高效监测环境热点
Adaptive Sensor Placement Inspired by Bee Foraging: Towards Efficient Environment Monitoring
- 融合蜂群算法与莱维飞行，动态优化传感器部署位置
- 显著提升关键热点区域的识别准确率，优于传统方法
- 适用于环境监测与搜救等多领域，具强泛化能力
本文面向可持续机器人未来，针对环境监测与精准农业等任务中的高效算法需求，提出一种结合人工蜂群算法（ABC）与莱维飞行的混合优化算法，用于自适应传感器部署，并引入领域专家定义的热点区域概念。该方法通过增强探索与利用能力，显著提升对关键热点区域的识别效果。实验表明，该算法在复杂场景中具备优越性能，可推广至更广泛的搜索与救援等优化问题应用场景。
原文摘要 · Abstract (English)
This paper aims to make a mark in the future of sustainable robotics, where efficient algorithms are required to carry out tasks like environmental monitoring and precision agriculture efficiently. We proposed a hybrid algorithm that combines Artificial Bee Colony (ABC) with Levy flight to optimize adaptive sensor placement alongside an important notion of hotspots from domain knowledge experts. By enhancing exploration and exploitation, our approach significantly improves the identification of critical hotspots. This algorithm also finds its usecases for broader search and rescue operations applications, demonstrating its potential in optimization problems across various domains.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。