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