Integrated truss optimization using a ternary‐quantized gradient method with implicit topology control

This paper proposes a structural optimization framework referred to as the ternary-quantized gradient (TQG) method. Departing from the prevailing assumption of a fixed design variable dimension during the search, it performs integrated optimization of size, shape, and topology without pre-specifying the dimension. The proposed method combines a single-agent search scheme, zeroth-order optimization, a Leaky ReLU-based penalty function, and an additional exploration strategy to enable efficient and automated design space exploration through implicit topology control. The proposed method was validated through optimization of a truss cantilever and truss girder, representing stiffness- and strength-governed structures, respectively. In both cases, TQG method successfully determined the optimal panel count while simultaneously optimizing size and shape, producing results comparable to those obtained by well-known metaheuristic algorithms under predefined topology settings. The proposed method was applied to the early-stage decision-making process of high-rise building design, optimizing panel count and configuration to efficiently resist lateral loads while satisfying serviceability constraints. These results demonstrate that the proposed TQG method can optimize the number of design variables through implicit topology control while achieving integrated optimization of size, shape, and topology in a single run, offering a practical and efficient approach for early-stage structural design.