Multi-objective structural optimization for the automatic member grouping of truss structures using evolutionary algorithms

This paper aims to formulate the multi-objective structural optimization problem to find the best member group-ing of truss structures. The weight of the structure and the different number of discrete cross-sectional areas are the conflicting objective functions to be minimized simultaneously, generating a Pareto front presenting the non-dominated solutions. Sixteen multi-objective evolutionary algorithms are adopted to solve the proposed optimization problems. Several benchmark problems and a new proposed optimization problem are analyzed. Even though original groupings based on symmetry are defined, it is possible to discover other competitive structural member configurations that can interest decision-makers regarding manufacturing, cutting, transportation, checking, and welding.

Automated summary

This paper formulates a multi-objective structural optimization problem and utilizes multiple evolutionary algorithms to solve it. By optimizing the grouping of structural members, the best truss structure can be found. After analyzing various benchmark problems, the study reveals the existence of competitive structural member configurations beyond symmetry-based groupings.