Abstract: In the building industry, one of the key components to ensuring a project's successful completion is multi-objective project management. However, due to its own limitations, the traditional multi-objective management approach for projects is no longer able to meet the requirements of building construction and urgently needs to be improved. This is because the construction industry is becoming more competitive and construction standards are improving. Traditional methods for multi-objective optimization typically involve simply summing multiple objectives with weights, overlooking the interdependencies among these objectives. These methods often get trapped in local optimal solutions and rely heavily on predefined models and parameters, limiting their adaptability to sudden changes during the construction process. Therefore, a multi-objective management approach based on multi-objective genetic algorithm for construction projects is proposed. It enables in-depth analysis and comprehensive optimization of the complex relationships between objectives, leading to more informed decisions. By facilitating rapid iteration and adaptation, it enables timely adjustments and optimizations to ensure that project goals remain consistent in complex and dynamic environments. In the experimental validation, the NSGA-II algorithm achieved a significant accuracy of 0.642 and success rate of 0.504 on the VOT dataset, both of which improved by about 1.0% and 0.6% compared to the comparison algorithm. Experimental results on the TrackingNet dataset revealed that the algorithm achieved an accuracy of 0.791 and a success rate of 0.763, while it still maintained an accuracy of 0.542 and a success rate of 0.763 in the face of occlusion. The enhanced multi-objective genetic algorithm had higher accuracy and success rates. This demonstrates the efficiency and excellence of the multi-objective management optimization approach suggested in this study for building projects. The research results have some application value in the multi-objective optimization of engineering projects.

Yong Yang and Jinrui Men, “Multi-Objective Optimization of Construction Project Management Based on NSGA-II Algorithm Improvement” International Journal of Advanced Computer Science and Applications(IJACSA), 16(1), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160143

@article{Yang2025,
title = {Multi-Objective Optimization of Construction Project Management Based on NSGA-II Algorithm Improvement},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160143},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160143},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {1},
author = {Yong Yang and Jinrui Men}
}