The approach, validated using Finite Element Model data and real-world measurements, showed improved accuracy and computational efficiency in identifying structural damage. Other researchers considered the use of Differential Evolution (DE) to obtain improved steel frames, as for example, [PERSON] and [PERSON] (2018) and [PERSON] et al. (2019). [PERSON] and [PERSON] (2018) proposed a constrained differential evolution (iCDE) algorithm for the optimization of steel frames with discrete design variables, whereas [PERSON] et al. (2019) proposed a framework for sizing optimization of steel structures using a combination of an improved Differential Evolution approach (2 EpDE), Multi-Comparison Technique (MCT), and Promising Individual Method (PIM). Although not for frame structures, [PERSON] et al. (2019) conducted a comparison study among some metaheuristic algorithms, for example, the DE and the covariance matrix adaptation evolution strategy (CMA-ES) for size optimization of truss structures under natural frequency constraints. These authors concluded that the latter presented the best performance and the best optimal solutions for the design of the truss structures studied. Optimization studies considering more than one objective function have also been considered. For example, the minimization of mass and nodal displacement of truss structures was addressed by ([PERSON] et al., 2019) through the Two Archive-boosted Multi-Objective Hippocamp Optimization Algorithm. The solutions were compared with results obtained via other multi-objective algorithms showing good performance. Near-optimal solutions alongside Pareto-optimal solutions in the standardization process of a multi-objective structural design were obtained by ([PERSON] et al., 2019) highlighting how standard section selection may influence objective function values. The authors combined a modified Particle Swarm Optimization method and a computationally efficient standardization algorithm, showing that considering near-optimal solutions can reveal superior final designs. Other multiobjective optimization published works in this context are due to [PERSON] et al. (2019), who presented a comparative study focused on structural steel buildings using the Non-dominated Sorting Genetic Algorithm (NSGA-II) and Particle Swarm Optimization (PSO). The authors concluded that although both approaches allowed obtaining an improved structural performance for the buildings, the PSO provided better solutions in general. Notably, the review on multi-objective optimization based on surrogate models for sustainable building design was recently presented by [PERSON] et al. (2019), who besides achieving the state of the art in this domain, pointed out possible development pathways. From the literature survey performed, it was possible to conclude that the number of published works considering clustering analysis in the structural analysis and optimization domains is scarce. This is evidenced by the survey presented by ([PERSON] et al., 2019). Only a very few works were found, for example, the one due to ([PERSON] et al., 2019) which proposed a cluster-based analysis method for the prediction of nonlinear properties of heterogeneous material. In another context, ([PERSON] et al., 2019) have used the K-means method to solve general nonlinear multiscale problems without using surrogate models. In both works, the dimensionality reduction of the problem was a major objective. The present work proposes an integrated framework that enables us to characterize sets of optimal solutions of steel-plane frame structures, using for that purpose the finite element method, the Red Fox Optimization technique, and the K-means method. This specific optimization technique is justified by the good performance it showed as demonstrated by [PERSON] and [PERSON] (2018) who proposed and tested it with different benchmark and engineering applications, but also because this technique showed a good performance in frame-type structures made of functionally graded materials ([PERSON] et al., 2019).