High connectivity and robustness are critical requirements in distributed networks, as they ensure resilience, efficient communication, and adaptability in dynamic environments. Additionally, optimizing energy consumption is also paramount for ensuring sustainability of networks composed of energy-constrained devices and prolonging their operational lifespan. In this study, we introduce an Artificial Intelligence (AI)-enhanced self-organizing network model, where each adaptive node autonomously adjusts its transmission power to optimize network connectivity and redundancy while lowering energy consumption. Building on our previous Hamiltonian-based methodology, which is designed to lead networks toward globally optimized states of complete connectivity and minimal energy usage, this research integrates a Multi-Layer Perceptron (MLP)-based decision-making model at each node. By leveraging a dataset from the Hamiltonian approach, each node independently learns and adapts its transmission power in response to local conditions, resulting in emergent global behaviors marked by high connectivity and resilience against structural disruptions. This distributed, MLP-driven adaptability allows nodes to make context-aware power adjustments autonomously, enabling the network to maintain its optimized state over time. Simulation results show that the proposed AI-driven adaptive nodes collectively achieve stable complete connectivity, significant robustness, and optimized energy usage under various conditions, including static and mobile network scenarios. This work contributes to the growing field of self-organizing networks by illustrating the potential of AI to enhance complex network design, supporting the development of scalable, resilient, and energy-efficient distributed systems across diverse applications.

翻译：高连接性与鲁棒性是分布式网络的关键需求，它们确保了动态环境下的韧性、高效通信与适应性。此外，优化能耗对于确保由能量受限设备组成的网络的可持续性并延长其运行寿命也至关重要。在本研究中，我们提出了一种人工智能增强的自组织网络模型，其中每个自适应节点自主调整其传输功率，以优化网络连接性与冗余度，同时降低能耗。本研究基于我们先前提出的哈密顿量方法——该方法旨在引导网络达到全局优化的完全连接与最小能耗状态——在每个节点集成了一个基于多层感知机的决策模型。通过利用来自哈密顿量方法的数据集，每个节点能够根据局部环境独立学习并调整其传输功率，从而涌现出以高连接性和对结构破坏的强韧性为标志的全局行为。这种分布式的、MLP驱动的自适应机制使得节点能够自主进行情境感知的功率调整，从而使网络能够随时间推移维持其优化状态。仿真结果表明，所提出的人工智能驱动自适应节点在各种条件下（包括静态与移动网络场景）能够协同实现稳定的完全连接、显著的鲁棒性以及优化的能耗。这项工作通过阐明人工智能在增强复杂网络设计方面的潜力，为自组织网络这一不断发展的领域做出了贡献，支持跨多种应用的可扩展、高韧性且高能效分布式系统的开发。