Intelligent Decision-Making Method Of Prefabricated Building Project Under Differential Particle Swarm Optimization Algorithm

The development of our prefabricated buildings is closely related to people’s needs.Prefabricated buildings refer to the pre-design and production of the components required for the building,then transport them directly to the construction site.Prefabricated buildings also connect the components through corresponding connection measures.Buildings constructed in this way have the advantages of safety and strong bearing capacity,and have the characteristics of standardized construction and information management.This thesis studies the construction design and project decisionmaking of prefabricated buildings,firstly discusses the advantages and disadvantages of prefabricated buildings in practical engineering applications by analyzing the advantages and disadvantages of prefabricated buildings,and will use optimization algorithms for prefabricated buildings project decisions.The innovation of the thesis lies in the combination of the differential optimization algorithm and the particle swarm optimization algorithm,and a three swarm differential particle swarm optimization algorithm is proposed.The conservative DE algorithm,the radical DE algorithm,and the PSO algorithm are used for parallel computing.The algorithm has high computational efficiency and can promote the communication between populations.Next,the classical test function is used to test the performance of the algorithm.After comparison,the three swarm difference particle swarm optimization algorithms have good computational accuracy and relatively good computational efficiency.In the actual case of affordable housing,this algorithm is used to make decisions on prefabricated building projects,and two decision-making schemes with the shortest construction period and the minimum cost loss are designed according to the requirements.Differential particle swarm algorithm and traditional genetic algorithm are used respectively.Calculate the optimal solution.By comparison,the three swarm difference particle swarm optimization algorithms proposed in this thesis achieve good results in the above cases.Finally,the following conclusions can be drawn through the research of this thesis:（1）the solution accuracy of the three swarm difference particle swarm optimization algorithms is relatively high,and the calculation results are the closest to the accurate value;（2）the algorithm has a fast convergence speed,especially in the case of solving multimodal function optimization,it can quickly converge with a small number of iterations;（3）The final results obtained by the three swarm difference particle optimization algorithms are relatively stable,indicating that they have strong robustness and strong adaptability of the algorithm.Therefore,the decision-making in the process of designing prefabricated buildings can be well executed;（4）Using this algorithm,the prefabricated building project has greatly improved the work efficiency or reduced the construction cost,making the prefabricated building project more efficient than before.The results show that the algorithm can be effectively applied to the decision-making of prefabricated buildings.