Structural optimization of unsymmetrical eccentric load steel box girder based on new swarm intelligence optimization algorithm

The steel box girder structure is widely used in many engineering practices and it is therefore necessary to study the optimization and improvement of its structure. Firstly, a small-tonnage double-girder bridge crane was used as an example, with the box section parameters for one of the main girders as the design variables and weight reduction as the objective. The mathematical model for the optimization of this validation case was developed with the strength, stiffness and stability of the girder as the efficiency constraints. The next step was to go through two new swarm intelligence algorithms based on animal hunting behavior, Grey Wolf Optimizer (GWO) and Whale Optimization Algorithm (WOA), as well as two classical swarm algorithms, Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The optimization models of these four algorithms were simulated and analyzed by the finite element method, and the simulation results were compared. The feasibility and performance of these two new swarm intelligence algorithms in the structural optimization of asymmetric eccentrically loaded box girders were verified. Finally, these two new swarm intelligence algorithms were applied to the structural optimization of the main girder of a large-tonnage bridge crane.