## 5 Conclusions and Future Work  
Developing models for predicting and improving a project's labor productivity is challenging because of the complexity of construction projects. Hence, accurate CLP prediction is required for effective decision making before and during project execution. The fact that numerous factors affect CLP is the main challenge in modeling labor productivity. This study deals with this challenge by developing an HFS method prior to CLP prediction. The main aim of this study was to develop a novel approach for predicting and optimizing CLP. After developing the HFS method, this novel methodology developed and compared four different predictive models, using ANN, ANFIS, ANFIS-GA, and RF and the 14 factors selected by HFS as inputs and CLP as an output. The comparative analysis of four predictive models showed that the RF model obtained better accuracy, compared with the three other models. Then, RF as the most accurate predictive model was selected to integrate with PSO for identifying the optimum value of influential factors and the maximum CLP value. The proposed HFS-RF-PSO model has the capability of obtaining the optimized CLP close to a company's preferred value and minimizing deviation of the predicted CLP factors from the average value of factors in a data set. Based on the results of the HFS-RF-PSO model using the mentioned data set, among 14 selected factors, \"Level of interruption and disruption,\" \"Working condition (dust and fumes),\" and \"Fairness in performance review of crew by foreman\" have the largest deviation from their average value, which means major improvements regarding these factors are needed in order to obtain optimum CLP. Furthermore, comparing the four most common ML models highlighted some critical modeling features of the presented models, which can assist researchers in future studies. The contributions of this study include (1) identifying the most predictive factors for CLP by developing an HFS model that contains the integration of Relieff and SVM-GA, (2) developing and comparing four different predictive models for CLP and identifying the most accurate model, and (3) developing a novel approach--the HFS-RF-PSO algorithm--for optimizing the factors that influence CLP and identifying the maximum CLP value considering the minimum deviation from targeted CLP value and also finding the optimum value of the selected factors based on minimizing their deviation from their average value in the data set. The proposed HFS-RF-PSO model helps project managers predict, optimize, and improve the CLP value, taking into account the factors that are the most predictive of CLP. Although construction projects are unique and the factors affecting CLP may differ from project to project, the proposed model is flexible, and new influencing factors can be added to the existing model structure to predict and optimize CLP and its factors for a given project. The results of this study and implementation of the HFS-RF-PSO model will help project managers identify causes of low labor productivity and select and prioritize corrective measures based on the deviation of factors in the model, to improve CLP. The model also enables project managers to improve the reliability of predictions. Future research can focus on using the proposed methodology to model and optimize multifactor construction productivity, which includes labor, equipment, and materials. Furthermore, future studies can present corrective measures to improve CLP, according to the HFS-RF-PSO results that show which factors need the most changes for reaching the targeted CLP. In addition, using new field data from other labor-dependent activities will help researchers overcome one of the mentioned limitations of this study and develop a generic hybrid model for predicting and optimizing CLP. Conceptualization, S.E. ; methodology, S.E. ; formal analysis, S.E. ; software, investigation, S.E. ; writing--original draft preparation, S.E. ; writing--review and editing, S.E. ; conceptualization, A.R.F.