发表机构
Polytechnique Montréal（蒙特利尔理工学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对战术无线网络设计的组合优化难题，提出结合图神经网络的学习增强禁忌搜索算法，通过搜索轨迹信息减少目标评估，提升计算效率与解质量。
AI 中文摘要
在实际作战约束下设计高性能战术无线网络会产生极具挑战性的组合优化问题，其中候选解的评估依赖于详细的物理模型和感知流量的模型。尽管禁忌搜索（Tabu Search）等经典元启发式算法能为探索大型搜索空间提供有效机制，但其计算成本仍然很高，因为每次迭代都必须评估大量候选移动方案。本文提出一种数据驱动框架，通过学习指导禁忌搜索的移动选择过程来提升其效率。我们的方法不改变邻域结构，而是利用优化过程中生成的搜索轨迹所包含的信息。每次迭代中，我们记录基于边的改进型和非改进型变换，以及一组捕捉网络结构、几何和性能特征的描述性特征。这些信息用于训练图神经网络（Graph Neural Network, GNN），以预测候选移动方案对目标函数的影响。训练后的模型被集成到禁忌搜索算法中，根据预测质量对候选变换进行排序，从而在保持对搜索空间有效探索的同时，减少计算成本高昂的目标函数评估次数。在合成基准实例上的实验结果表明，所提出的学习辅助禁忌搜索算法显著减少了计算时间，且始终比标准算法产生更高质量的解。这些发现凸显了将机器学习与元启发式算法相结合的潜力，即利用搜索轨迹中嵌入的隐式知识，为大规模网络设计问题开发更高效的求解方法铺平了道路。
英文摘要
Designing high-performance tactical wireless networks under realistic operational constraints gives rise to challenging combinatorial optimization problems, where the evaluation of candidate solutions relies on detailed physical and traffic-aware models. Although classical metaheuristics such as Tabu Search offer effective mechanisms for exploring large search spaces, their computational cost remains high because numerous candidate moves must be evaluated at every iteration. In this paper, we propose a data-driven framework that improves the efficiency of Tabu Search by learning to guide its move selection process. Rather than altering the neighborhood structure, our approach exploits the information contained in the search trajectories generated during the optimization process. At each iteration, we record both improving and non-improving edge-based transformations together with a set of descriptive features capturing the structural, geometric, and performance characteristics of the network. This information is used to train a Graph Neural Network (GNN) that predicts the impact of candidate moves on the objective function. The trained model is then integrated into the Tabu Search algorithm to rank candidate transformations according to their predicted quality, thereby reducing the number of costly objective evaluations while maintaining an effective exploration of the search space. Experimental results on synthetic benchmark instances demonstrate that the proposed learning-assisted Tabu Search notably reduces computation time while consistently producing higher-quality solutions than the standard algorithm. These findings highlight the potential of combining machine learning with metaheuristics by leveraging the implicit knowledge embedded in search trajectories, paving the way for more efficient solution methods for large-scale network design problems.
