Autonomous Underwater vehicles must operate in strong currents, limited acoustic bandwidth, and persistent sensing requirements where conventional swarm optimisation methods are unreliable. This paper presents NOAH, a novel nature-inspired swarm optimisation algorithm that combines current-aware drift, irreversible settlement in persistent sensing nodes, and colony-based communication. Drawing inspiration from the behaviour of barnacle nauplii, NOAH addresses the critical limitations of existing swarm algorithms by providing hydrodynamic awareness, irreversible anchoring mechanisms, and colony-based communication capabilities essential for underwater exploration missions. The algorithm establishes a comprehensive foundation for scalable and energy-efficient underwater swarm robotics with validated performance analysis. Validation studies demonstrate an 86% success rate for permanent anchoring scenarios, providing a unified formulation for hydrodynamic constraints and irreversible settlement behaviours with an empirical study under flow.

翻译：自主水下航行器必须在强洋流、有限声学带宽及持续传感需求等复杂环境下运行，传统群体优化算法在此类场景中可靠性不足。本文提出一种新型仿生群体优化算法NOAH，该算法融合了洋流感知漂移、持续传感节点的不可逆沉降以及群体通信机制。受藤壶无节幼体行为启发，NOAH通过提供流体动力学感知、不可逆锚定机制和群体通信能力，有效解决了现有群体算法在水下探测任务中的关键局限性。该算法为可扩展、高能效的水下群体机器人技术建立了完整理论基础，并通过性能验证分析得到实证。验证研究表明，该算法在永久锚定场景中达到86%的成功率，通过流体环境下的实证研究，为流体动力学约束与不可逆沉降行为提供了统一的理论框架。