This paper presents a hybrid optimization methodology integrating Biased Random-Key Genetic Algorithms (BRKGA), data mining, and reinforcement learning to address the Sensor Network Optimization Problem. Using statistical and clustering techniques, the method enhances BRKGA by incorporating patterns extracted from elite solutions. A reinforcement learning agent dynamically decides when and how to mine patterns, enhancing adaptivity and optimizing the process. The validated patterns are then integrated into a Quadratic Knapsack Problem (QKP) optimization model, guaranteeing compliance with budget constraints while efficiently exploring potential regions of the solution space. Experimental results demonstrate the approach's effectiveness in generating high-quality solutions for sensor allocation in Wireless Sensor Networks (WSNs), balancing exploration and exploitation, and achieving robust performance in complex optimization scenarios.

Optimizing vehicle routing is essential for logistics efficiency, but companies often operate independently due to competitive constraints, limiting potential gains from collaboration. In this work, we introduce a federated-inspired optimization approach for the Capacitated Vehicle Routing Problem (CVRP), where companies share optimized hyperparameters instead of raw data. Our method leverages Hybrid Genetic Search (HGS) for CVRP optimization while preserving data privacy. Unlike traditional federated learning, aggregation occurs outside the federated-inspired process, focusing on hyperparameter tuning rather than model updates. We evaluate different heuristic aggregation strategies, including FedAvg, Trimmed Mean, and Majority Vote. Experiments were conducted using CVRPLIB benchmark instances, varying the number of companies and problem instances to assess scalability and effectiveness. Our results indicate that federated-inspired hyperparameter aggregation can offer advantages over isolated optimization, particularly in reducing total routing costs. These findings suggest that controlled hyperparameter sharing may enhance logistics decisionmaking while maintaining operational autonomy.

Green infrastructure plays a pivotal role when combating the temperature rises caused by climate change. In addition, in the context of smart and sustainable cities, those spaces, such as urban forests, can be used as places for tours and physical activities in contact with nature, even in big urban areas. However, as the number of people using those spaces increases, so does the risk that an individual gets lost. This kind of incident must be quickly addressed in order to avoid severe health outcomes to the individual. Those rescue operations mobilize firefighters and expensive equipment, such as helicopters, making them extremely costly to public authority. In order to reduce the search time in those rescue operations, this paper considers the Data Mule Routing Problem with Limited Autonomy (DMRP-wLA). This research proposes a hybrid algorithm, composed of an Ant Colony Optimization algorithm enhanced by data mining techniques, to find high-quality solutions to the highlighted problem. Experimental results indicate that ACO-DM consistently outperforms traditional ACO approaches, achieving the best average solution costs in 11 out of 15 evaluated scenarios. In comparison with existing algorithms in the literature, the proposed ACO-DM approach provided substantial cost reductions—between 20 % and 60 % in large-scale instances—while maintaining practical UAV operational times.

This study introduces a novel approach to the Sensor Allocation Problem (SAP) in Wireless Sensor Networks (WSNs) by integrating reinforcement learning with a multi-centrality heuristic. The goal of SAP is to optimize the network topology to minimize energy consumption while maintaining connectivity. We propose a reinforcement learning agent that interacts with a multi-centrality heuristic to dynamically select sensor placement. Extensive computational experiments were performed on both regular and semi-regular grid topologies of varying sizes. Comparisons with a Mixed-Integer Linear Programming (MILP) model reveal that our approach not only yields high-quality solutions in small to medium instances but also remains effective in larger networks, where the MILP solver often fails to produce feasible solutions. Notably, the RL module consistently improves upon the baseline heuristic allocation, demonstrating significant cost reductions while preserving full network coverage. By uniting a centrality-driven heuristic with an adaptive learning mechanism, the proposed hybrid framework addresses challenges in SAP more effectively than traditional exact methods. Consequently, it provides a promising avenue for large-scale WSN deployments, maintaining robust connectivity and efficiently managing energy consumption across various network configurations.

In this paper, we aim to reduce the number of nodes from Graph Neural Networks (GNNs), thereby simplifying models and reducing computational costs. GNNs are highly effective for various tasks, such as prediction, classification, and clustering, due to their ability to learn node and edge attributes and relationships, and they have been utilized for intelligent transportation systems recently by converting sensor networks into graph structures. Deep spatio-temporal neural networks, including Spatio-Temporal Graph Convolutional Networks (STGCNs), capture spatial and temporal dependencies, making them suitable for traffic speed forecasting, traffic demand prediction, and travel time estimation. Despite their success, GNNs face challenges in industrial applications due to significant memory usage and time consumption. In this paper, we propose a new approach to node reduction that outperforms existing methods in computational efficiency. Our experiments on two real-world traffic datasets demonstrate that using the heuristic and edge information to reduce nodes can cut computation time of optimization up to 95% and, by eliminating noise, can even enhance prediction accuracy.

Wireless sensor networks are widely valued for their effectiveness in real-time data collection. As the amount of data exchanged within such networks grows, designing a robust network topology that maximizes area coverage with minimal sensors has become a critical challenge. The choice of topology impacts key network metrics, including sensor coverage, communication range, connectivity, inference, and installation and management costsIn this paper, we address the Wireless Sensor Network Planning Problem with Multiple Sources/Destinations, presenting an optimization approach based on deep reinforcement learning. This problem is noteworthy, as sensors in various applications are often required to share data within distinct destinationsWe leverage deep reinforcement learning to effectively address the complex task of selecting optimal sensor locations. Our reinforcement learning agent dynamically learns network structure by iteratively adding and removing sensors, optimizing both sensor coverage and the total number of sensors used. Experiment across diverse scenarios demonstrate the effectiveness of our method for network planning problems of varying scales, achieving full coverage with fewer sensors than traditional approaches. Additionally, our approach also produce solutions for large instances where Mixed Integer Programming solvers were not able to. Overall, our method was able to reduce the number of sensors used by up to 22.3% compared to other methods.

This study addresses the Wireless Sensor Network Planning Problem with Multiple Sources/Destinations (WSNPMSD), an optimization challenge focused on reducing the sensor count within a network topology for a specified area, considering numerous sources and destinations. We introduce a hybrid strategy for tackling WSNP-MSD, particularly effective for large-scale scenarios, combining a Biased Random-key Genetic Algorithm with a Local Branching Technique. This methodology is justified by the limitations exact methods may encounter when the number of variables increases. Through computational experiments, we demonstrate the superiority of our proposed method over conventional exact methods in managing large instances of the WSNP-MSD.