Machine Learning-Enhanced Tabu Search for Tactical Wireless Network Design

• URL: http://arxiv.org/abs/2608.28627v1
• Date: Fri, 31 Jul 2026 15:03:53 GMT
• Title: Machine Learning-Enhanced Tabu Search for Tactical Wireless Network Design
• Abstract summary: We propose a data-driven framework that improves the efficiency of Tabu Search by learning to guide its move selection process.<n>We record both improving and non-improving edge-based transformations together with a set of descriptive features.<n>This information is used to train a Graph Neural Network (GNN) that predicts the impact of candidate moves on the objective function.
• Score: 0.688204255655161
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: 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.

Related papers

• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• GASE: Graph Attention Sampling with Edges Fusion for Solving Vehicle Routing Problems [6.084414764415137]
  We propose an adaptive Graph Attention Sampling with the Edges Fusion framework to solve vehicle routing problems. Our proposed model outperforms the existing methods by 2.08%-6.23% and shows stronger generalization ability.
  arXiv  Detail & Related papers  (2024-05-21T03:33:07Z)
• Reinforced In-Context Black-Box Optimization [64.25546325063272]
  RIBBO is a method to reinforce-learn a BBO algorithm from offline data in an end-to-end fashion. RIBBO employs expressive sequence models to learn the optimization histories produced by multiple behavior algorithms and tasks. Central to our method is to augment the optimization histories with textitregret-to-go tokens, which are designed to represent the performance of an algorithm based on cumulative regret over the future part of the histories.
  arXiv  Detail & Related papers  (2024-02-27T11:32:14Z)
• Frog-Snake prey-predation Relationship Optimization (FSRO) : A novel nature-inspired metaheuristic algorithm for feature selection [0.0]
  This study proposes the Frog-Snake prey-predation Relationship Optimization (FSRO) algorithm. It is inspired by the prey-predation relationship between frogs and snakes for application to discrete optimization problems. The proposed algorithm conducts computational experiments on feature selection using 26 types of machine learning datasets.
  arXiv  Detail & Related papers  (2024-02-13T06:39:15Z)
• Sensitivity-Aware Mixed-Precision Quantization and Width Optimization of Deep Neural Networks Through Cluster-Based Tree-Structured Parzen Estimation [4.748931281307333]
  We introduce an innovative search mechanism for automatically selecting the best bit-width and layer-width for individual neural network layers. This leads to a marked enhancement in deep neural network efficiency.
  arXiv  Detail & Related papers  (2023-08-12T00:16:51Z)
• Online Network Source Optimization with Graph-Kernel MAB [62.6067511147939]
  We propose Grab-UCB, a graph- kernel multi-arms bandit algorithm to learn online the optimal source placement in large scale networks. We describe the network processes with an adaptive graph dictionary model, which typically leads to sparse spectral representations. We derive the performance guarantees that depend on network parameters, which further influence the learning curve of the sequential decision strategy.
  arXiv  Detail & Related papers  (2023-07-07T15:03:42Z)
• Batch Active Learning from the Perspective of Sparse Approximation [12.51958241746014]
  Active learning enables efficient model training by leveraging interactions between machine learning agents and human annotators. We study and propose a novel framework that formulates batch active learning from the sparse approximation's perspective. Our active learning method aims to find an informative subset from the unlabeled data pool such that the corresponding training loss function approximates its full data pool counterpart.
  arXiv  Detail & Related papers  (2022-11-01T03:20:28Z)
• Improved Algorithms for Neural Active Learning [74.89097665112621]
  We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. We introduce two regret metrics by minimizing the population loss that are more suitable in active learning than the one used in state-of-the-art (SOTA) related work.
  arXiv  Detail & Related papers  (2022-10-02T05:03:38Z)
• A Differentiable Approach to Combinatorial Optimization using Dataless Neural Networks [20.170140039052455]
  We propose a radically different approach in that no data is required for training the neural networks that produce the solution. In particular, we reduce the optimization problem to a neural network and employ a dataless training scheme to refine the parameters of the network such that those parameters yield the structure of interest.
  arXiv  Detail & Related papers  (2022-03-15T19:21:31Z)
• Ranking Cost: Building An Efficient and Scalable Circuit Routing Planner with Evolution-Based Optimization [49.207538634692916]
  We propose a new algorithm for circuit routing, named Ranking Cost, to form an efficient and trainable router. In our method, we introduce a new set of variables called cost maps, which can help the A* router to find out proper paths. Our algorithm is trained in an end-to-end manner and does not use any artificial data or human demonstration.
  arXiv  Detail & Related papers  (2021-10-08T07:22:45Z)
• Fitting the Search Space of Weight-sharing NAS with Graph Convolutional Networks [100.14670789581811]
  We train a graph convolutional network to fit the performance of sampled sub-networks. With this strategy, we achieve a higher rank correlation coefficient in the selected set of candidates.
  arXiv  Detail & Related papers  (2020-04-17T19:12:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.