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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 4, 2024.
Abstract: In order to meet the development needs of the construction engineering industry and further optimize and improve the talent training mode, this paper studies the talent training model of high-rise construction project management under the multi-objective evolutionary algorithm. The cognitive ability model of management talent is constructed, and the learning ability of management talent is analyzed. With the optimization objectives of minimizing the construction period, minimizing the project cost, and maximizing the benefit of skill growth in high-rise building projects, and taking the conditions of average proficiency and average duration of construction as constraints, the mixed immune genetic algorithm with the introduction of the double-island model is adopted to carry out multi-objective evolution of management talent training, so as to obtain the best training scheme for management talent in high-rise building projects. The experimental results show that after the optimization of this model, the skill proficiency of project management personnel can be effectively improved, construction time can be effectively reduced, construction efficiency can be improved, and construction costs can be improved.
Pan QI, “Training Model of High-Rise Building Project Management Talent under Multi-Objective Evolutionary Algorithm” International Journal of Advanced Computer Science and Applications(IJACSA), 15(4), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150426
@article{QI2024,
title = {Training Model of High-Rise Building Project Management Talent under Multi-Objective Evolutionary Algorithm},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150426},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150426},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {4},
author = {Pan QI}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.