Introduced in 2021 [8], the RFO algorithm has been successfully applied across various fields, including computer science, materials science, and engineering. In this latter case, it has been found in application fields such as chemical systems, fuel cells, and functionally graded structures. In the computer science area, a method for detecting malware on Android smartphones based on an algorithm adapted from the RFO was presented by [9], while in [10], RFO was used to optimize hyperparameters in deep learning models. In structural engineering, as far as results from the literature survey, this method was used to optimize the parameters of structures made of functionally graded material [11]. Other engineering areas were covered in [12] where the technique was used to optimize the equilibrium phase and stability of chemical systems, and in [13] to obtain the optimum parameters for solid oxide fuel cells. In addition, various models have been proposed that combine the RFO algorithm, especially due to its global optimization capabilities, with other metaheuristics that can complement it, depending on the authors' objectives. For example, [PERSON] et al. [14] proposed a model that combines the RFO algorithm with deep-learning-enabled microarray gene expression classification model, which allowed to identify various classes for high-dimensional and small-scale microarray data. In [PERSON] and [PERSON] [15], the Red Fox Optimization and Cluster-based Routing algorithm were combined to facilitate secure and energy-efficient data transmission among sensor nodes. The authors concluded that the proposed protocol performed better than the current routing techniques concerning security robustness, energy efficiency, and network lifetime. In another context, an intelligent waste management system through which waste can be sorted was proposed in [16] through an automated model that combines RFO with a dense network model. Metaheuristics have also been increasingly used in the broad structural engineering area, with applications that involve, for instance, reducing the weight and cost of steel structures and/or optimizing cross-sectional dimensions and material properties. In a recent overview [17] that illustrates this trend, a Genetic Algorithm-based optimization process was proposed to enhance the design of steel exoskeletons for seismic retrofitting. To achieve this, the study focused on optimizing the spatial arrangement and components' sizing to minimize the global weight and cost while ensuring structural integrity. The authors achieved significant improvements in retrofit efficiency across various case studies. [PERSON] et al. [18] introduced the Improved Marine Predators Algorithm for optimizing the size and shape of truss structures under natural frequency constraints, aiming to minimize weight while preventing resonance and reducing vibrations. The algorithm demonstrated good performance in comparison to a set of other metaheuristics across various truss structures. [PERSON] et al. [19] used a real-coded Genetic Algorithm integrated with the Bin Packing Problem to reduce waste by up to 40% across various 2D and 3D steel structures. A paradigm shift, from minimum weight targeting towards minimizing material waste and promoting standardization in steel structures, was proposed. [PERSON] et al. [20] introduced the so-called Improved Sine Cosine Algorithm for optimizing the size, shape, and topology of truss structures by incorporating a nonlinear conversion parameter, Levy flight for global search, an elite guidance strategy for local search, and a greedy selection mechanism. The authors concluded that the technique performed well for truss structure optimization. In a work joining metaheuristics and deep neural networks, [21] considered the African Vulture Optimization Algorithm with Deep Neural Networks to enhance damage detection in large-scale bridges, leveraging the first algorithm's ability to autonomously adjust parameters and optimize the neural network weights and biases.