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.

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.