Research on Optimization of Stacking Hoisting Construction of Assembled Steel Structure Based on Differential Evolution Particle Swarm Optimization

As a key link in construction, stacking hoisting faces many challenges, such as component matching, installation sequence optimization and error control. Differential Evolution Particle Swarm Optimization (DEPSO) algorithm combines the advantages of differential evolution (DE) and particle swarm optimization (PSO), showing high efficiency and robustness in solving complex optimization problems. In this study, DEPSO is used to optimize component matching, installation sequence and error control, and to find the optimal hoisting sequence and component matching scheme through intelligent optimization ability. The fitness function comprehensively considers the matching degree of components, the efficiency of installation sequence and the effect of error control, and realizes the algorithm through the steps of population initialization, fitness calculation, particle update and differential operation. In addition, in order to overcome the premature problem, a mutation mechanism based on fitness difference is introduced. The case study shows that after optimization, the matching degree of components is improved by 20%, the total hoisting time is reduced by 15%, the moving distance of crane is reduced by 10%, the time cost is reduced by 15.4%, and the safety risk index is reduced by 15.3%. The optimization measures effectively restrain the error accumulation and ensure the high accuracy and stability of the structure. The research results show that DEPSO algorithm has significant potential and application value in the optimization of stacked box hoisting construction of assembled steel structures.