面向BIM工程精细化造价预测的量子蜂群优化算法研究

Research on quantum bee colony optimization algorithm for precision cost prediction of BIM engineering

为提升工程资金使用的合理性,实现经济效益最优化,提出基于量子蜂群优化的BIM工程精细化造价预测算法。通过CATIA软件建立工程的参数化BIM模型,获取工程数据信息;将其作为输入,通过支持向量机(SVM)模型输出工程精细化造价预测结果;将SVM模型的惩罚参数和核函数参数作为人工蜂群算法中的蜂源,利用细粒度方式通过寻找最优适应度蜜源,对SVM工程造价预测模型参数进行优化;通过量子编码方式,在寻优最佳蜜蜂个体过程中编码个体位置,增强搜索精度,利用具备最佳参数的SVM获取最佳工程精细化造价预测结果。实验结果表明,该方法可以真实地构建工程BIM模型,获取工程数据信息;工程精细化造价预测精度高、误差小;在很强的外界干扰情况下,始终保持较高的工程造价预测精度。

In order to enhance the rationality of engineering fund utilization and achieve optimal economic benefits,a refined cost prediction algorithm for BIM engineering based on quantum bee colony optimization was proposed.A parameterized BIM model for engineering was established using CATIA software,and a large amount of engineering data information was obtained.The support vector machine(SVM)model was used to process the input engineering data and output refined cost prediction results.The penalty parameters and kernel function parameters of the SVM model were employed as peak sources in the artificial bee colony algorithm to optimize the parameters of the SVM engineering cost prediction model.By employing fine-grained methods,the optimal fitness honey source was found.Quantum coding was utilized to enhance the search accuracy during the process of searching for the best bee individual.The best parameters for the SVM model were obtained by searching for the best fitness honey source in a fine-grained manner.The SVM with the best parameters yielded the best engineering refined cost quota prediction result.Experimental results demonstrated that this method could effectively construct engineering BIM models and obtain accurate engineering data information.The precision of engineering cost prediction was high with minimal errors.Moreover,it maintained a consistently high level of accuracy in engineering cost prediction even under strong external interference.