Achieving efficiency in truss structural design using opposition-based geometric mean optimizer

This study introduces a novel technique for optimizing structural designs, focusing on creating lightweight structures that meet specific constraints. The main objective is to develop a new model for mass optimization in structural trusses using the opposition-based geometric mean optimizer (oGMO). This model combines the geometric mean optimizer (GMO) with the opposition-based learning (OBL) mechanism, enabling effective optimization of truss layouts utilizing both discrete and continuous variables. The efficacy of the oGMO model is assessed through various scenarios, including 25-bar and 72-bar spatial truss structures. These comprehensive assessments illustrate the effectiveness of the model, indicating that oGMO consistently produces superior designs compared to existing methods. Moreover, oGMO demonstrates remarkable computational efficiency, positioning it as a promising tool for structural design optimization, especially for achieving lightweight truss configurations.