An Improved PSO-Based Approach for Automated Form-Finding of Cable-Truss Structures

Determining the compatible prestress and geometry under self-weight constitutes a key challenge in the form-finding of cable-truss structures. To overcome the limitations of experience-dependent trial methods and enhance computational efficiency, this paper proposes an automated and integrated methodology by synergistically combining a simplified mechanical model with an improved Particle Swarm Optimization (PSO) algorithm. The core of the method lies in formulating the form-finding process as an optimization problem, where the horizontal inclination angles of the lower chord cables serve as the design variables for all radial cable-truss frames. To efficiently solve this high-dimensional optimization problem, an improved PSO algorithm, which introduces logistic chaotic mapping for particle initialization and a mutation operator within the iterative loop. Ablation studies confirm the individual contribution of each algorithmic enhancement. The algorithm intelligently searches for the optimal angle set, thereby simultaneously resolving the prestress and geometry. The proposed approach is rigorously validated through two representative numerical examples: a circular Type I and an elliptical Type II cable-truss, considering both cases with and without self-weight. The results demonstrate that the improved PSO-based solution achieves prestress distributions and nodal coordinates in excellent agreement with established benchmark data. More importantly, it attains this high precision with significantly reduced computational cost in terms of particle swarm size and iteration number. In conclusion, this improved PSO‑based approach provides an efficient, accurate, and automated tool for the integrated prestress‑geometry design of cable‑truss structures, demonstrating strong potential for practical engineering application.