Formulations of structural optimization problems are proposed in this paper to automatically find the best grouping of columns in 3D steel buildings. In these formulations, the conflicting objective functions, minimized simultaneously, are the weight of the structure and the number of different groups of columns. In other words, the smaller the number of different groups of columns, the greater the weight of the structure, and the greater the number of groups, the smaller the structure’s weight. The design variables are the bracing system configuration, column cross-section orientation, and assigned W-shaped profile indices for columns, beams, and braces. The design constraints are the allowable displacements, strength, and geometric considerations. After solving the multi-objective optimization problem, the result is a Pareto front, presenting non-dominated solutions. Three evolutionary algorithms based on differential evolution are adopted in this paper to solve three computational experiments. Even if preliminary groupings of columns are adopted, considering architectural aspects such as the symmetry of the structure, it is possible to discover other interesting structural configurations that will be available to the decision maker, who will be able to make their choices based on the impacts on manufacturing, cutting, transporting, checking and welding.

This paper aims to address the challenge of multi-objective structural optimization in the search for the most efficient configuration of members in truss structures. Conflicting objectives, such as the structure's weight, the number of discrete cross-sectional areas, the first natural vibration frequency, and the first load factor relative to overall structural stability, must be optimized simultaneously, resulting in a Pareto Front (PF) that provides non-dominated solutions. NSGA-III is the multi-objective evolutionary algorithm adopted to solve the proposed optimization problems. Non-dominated solutions are extracted from the FP using multi-criteria decision-making (MCDM). New competitive configurations of structural members can be discovered, offering attractive alternatives to decision-makers in manufacturing, cutting, transportation, checking, and welding.