基于改进布谷鸟算法的大型建设项目造价预测的建模与优化

Cost Prediction Modeling and Optimization of Large Construction Project Based on Improved Cuckoo Algorithm

在大型建设项目的决策阶段,结合工程量情况准确预测出工程造价可以促进项目顺利运转。为此,提出基于改进布谷鸟算法的大型建设项目造价预测模型与优化方法。结合云计算架构与特征,建立建筑类型、项目特征与指标规划等数据库表,分类工程数据;明确工程计量流程,分析损耗量与消耗量,计算消耗系数,实现消耗量定额;以工程造价数据为依据,采用支持向量机对非线性函数做升维处理,获取决策函数,构建预测模型;利用改进布谷鸟算法优化支持向量机参数,通过不断更新鸟巢位置,估计新鸟巢的适应度值,输出最佳预测结果。仿真实验表明,该模型预测精度高,能够降低项目造价预测成本,为企业增效。

In the decision-making stage of large-scale construction project, combining with the engineering quantity, accurately predicting the project cost can promote the development of the project. Therefore, the paper proposes a cost forecasting and optimization method for large-scale construction projects based on the improved cuckoo algorithm. Combined with the architecture and characteristics of cloud computing, the database tables of building types, project characteristics and index planning are established to classify the engineering data;the engineering measurement process is defined, the loss and consumption are analyzed, the consumption coefficient is calculated, and the consumption quota is realized. Based on the engineering cost data, the support vector machine is used to upgrade the dimension of the nonlinear function, obtain the decision function, and construct the prediction model. The improved cuckoo algorithm is used to optimize the parameters of support vector machine. By constantly updating the nest position, the fitness value of the new nest is estimated, and the best prediction result is output. The simulation results show that the model has high prediction accuracy, can reduce the cost of project cost prediction, and increase efficiency for enterprises.