A Time Compression Method for Landscape Architecture Construction Projects Based on Particle Swarm Optimization Neural Network

YAO Yongqiang

Abstract：In the process of landscape architecture construction project management,reliance upon a single neural network to address the duration compression model can readily result in the system attaining a local optimum,thereby engendering elevated compression costs.To solve the problem,a time compression method for landscape construction projects based on a particle swarm optimization neural network is proposed.It is imperative to acknowledge the intricacies of construction processes in landscape architecture construction,necessitating the establishment of a sub-network of construction projects.This sub-network can then be subjected to a schedule compression mechanism to construct a project group network planning framework.From a cost perspective,encompassing both rush and claim costs,a mathematical model for project duration compression is constructed with the objective of minimizing compression costs.The model also take into account resource constraints,duration compression constraints,and key path invariant constraints.The particle swarm optimization algorithm is employed to optimize neural networks and circumvent local optima.The data generated after simulating the implementation of multiple schedule compression schemes is analyzed,enabling more accurate predictions of changes in compression costs.Furthermore,the mathematical model of project schedule compression is solved to obtain the optimal compression strategy.The experimental results demonstrate that reliance on this method to compress the construction period of a specific landscape construction project resulted in a total compression cost of 550,000 yuan,which is a significant reduction in construction costs.