Improved Particle Swarm Optimization-Based Form-Finding Method for Suspension Bridge Installation Analysis

Abstract

In this paper, an improved particle swarm optimization (IPSO) method, which is based on standard particle swarm optimization (PSO) and a changing range genetic algorithm (GA), is proposed, and it is applied in the form-finding analysis of a suspension bridge installation. The new method has been integrated into the successive iteration method of the form-finding analysis, and it transfers the usual iterative process of form-finding analysis into a single objective optimization problem. The computation formulation and computer code of the IPSO have been developed and implemented, and benchmark examples are studied to test the performance of the IPSO method through numerical experiments. The results show that IPSO can be used as a viable solution methodology for the form-finding analysis in various initial, boundary, and complicated load conditions to overcome the limitations of the conventional Newton-Raphson (N-R) iterative method. Furthermore, compared with GA, PSO, and GA-PSO, IPSO has greater accuracy and efficiency. Finally, the method is applied in an actual form-finding analysis of a multitower suspension bridge (Yingwuzhou Yangtze River Bridge) to validate the practicality and feasibility of the IPSO-based method in engineering design. The case study indicates that IPSO provides superior convergent solutions for the analysis considered here when compared to the N-R method, and the computation time is affordable for large-scale engineering bridge construction design and analysis.