Application of Improved Frog Leaping Algorithm in Multi objective Optimization of Engineering Project Management
DOI:
https://doi.org/10.31181/dmame712024896Keywords:
SFLA, NSGA-II, Multi objective optimization, Project managementAbstract
The development of information has promoted the development of various industries, and the development of industries will inevitably lead to intensified competition, including the construction industry. To enhance the competitiveness of construction enterprises in the industry, a multi-objective optimization model for construction project management has been proposed. At the same time, carbon emission was included as one of the optimization objectives in the experiment. This can also align the construction industry with the concept of modern green development. A non-dominated sorting genetic algorithm with elite strategy was proposed to improve the hybrid frog leaping algorithm, and the improved hybrid frog leaping algorithm was used to solve multi-objective optimization problems. The improved hybrid frog leaping algorithm performed better in solving multi-objective optimization problems. The improved hybrid frog leaping algorithm found a total of 132 Pareto solution sets, while the non-dominated sorting genetic algorithm with elite strategy only found 23 Pareto solution sets. And the solution set of the improved hybrid frog leaping algorithm is closer to the optimal position. The optimized duration and cost of the improved hybrid frog leaping algorithm are lower, with an optimal duration of 135 days and a minimum cost of $20,000. A multi-objective optimization model for engineering project management incorporating carbon emissions was successfully constructed in the study, and the multi-objective optimization problem was solved.
Downloads
References
Tian, Y., Si, L., Zhang, X., Cheng, R., He, C., Tan, K. C., & Jin, Y. (2021). Evolutionary large-scale multi-objective optimization: A survey. ACM Computing Surveys (CSUR), 54(8), 1-34. https://doi.org/10.1145/3470971.
Atakul, N., & Gundes, S. (2021). Capital structure in the construction industry: Theory and practice. Canadian Journal of Civil Engineering, 48(7), 819-828. https://doi.org/10.1139/cjce-2019-0794.
Tran, D. H. (2020). Optimizing time-cost in generalized construction projects using multiple-objective social group optimization and multi-criteria decision-making methods. Engineering, 27(9), 2287-2313. https://doi.org/10.1108/ECAM-08-2019-0412.
Garrido, M., Madeira, J. F. A., Proenca, M., & Correia, J. R. (2019). Multi-objective optimization of pultruded composite sandwich panels for building floor rehabilitation. Construction and Building Materials, 198(FEB.20), 465-478. https://doi.org/10.1016/j.conbuildmat.2018.11.259.
Luo, S., & Guo, X. (2023). Multi-objective optimization of multi-microgrid power dispatch under uncertainties using interval optimization. Journal of Industrial and Management Optimization, 19(2), 823-851. https://doi.org/10.3934/jimo.2021208.
Kumar, A., Vohra, M., Pant, S., & Singh, S. K. (2021). Optimization techniques for petroleum engineering: A brief review. International Journal of Modelling and Simulation, 41(5), 326-334. https://doi.org/10.1080/02286203.2021.1983074.
Machairas, T. T., & Saravanos, D. A. (2022). Multi-objective optimization of a morphing structure incorporating shape memory alloy actuators. Journal of Intelligent Material Systems and Structures, 33(1), 84-104. https://doi.org/10.1177/1045389X211011675.
Deng, F., & Qin, N. (2022). An exploitation-enhanced multi-objective efficient global optimization algorithm for expensive aerodynamic shape optimizations. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering, 236(7), 1408-1421. https://doi.org/10.1177/09544100211032432.
Koranga, P., Singh, G., Verma, D., Chaube, S., Kumar, A., & Pant, S. (2018). Image denoising based on wavelet transform using Visu thresholding technique. International Journal of Mathematical, Engineering and Management Sciences, 3(4), 444. https://doi.org/10.33889/IJMEMS.2018.3.4-032.
Mohan, G. K., & Yarravarapu, S. R. (2020). An efficient design of finite impulse response-fractional-order differentiator using shuffled frog leaping algorithm heuristic. International Journal of Wavelets Multiresolution & Information Processing, 18(01), 73-77. https://doi.org/10.1142/S0219691319410054.
Xie, F., Hong, W., & Qiu, C. (2021). Speed fluctuation suppression of PMSM using active disturbance rejection and feedback compensation control. IET Electric Power Applications, 15(8), 1056-1057. https://doi.org/10.1049/elp2.12079.
Goli, A., Tirkolaee, E. B., & Weber, G. W. (2021). An integration of neural network and shuffled frog-leaping algorithm for CNC machining monitoring. Foundations of Computing and Decision Sciences, 46(1), 27-42. https://doi.org/10.2478/fcds-2021-0003.
Terapasirdsin, A., & Kiattisin, S. (2020). Crosstalk-aware global routing in VLSI design by using a shuffled frog-leaping algorithm. Journal of Mobile Multimedia, 16(1/2), 221-244. https://doi.org/10.13052/JMM1550-4646.161211.
Karakoyun, M. (2019). A new approach based on k-means clustering and shuffled frog leaping algorithm to solve the travelling salesman problem. Academic Perspective Procedia, 2(3), 446-453. https://doi.org/10.33793/acperpro.02.03.31
Ganguly, S. (2020). Multi-objective distributed generation penetration planning with load model using particle swarm optimization. Decision Making: Applications in Management and Engineering, 3(1), 30-42. https://doi.org/10.31181/dmame2003065g.
Rasoulzadeh, M., Edalatpanah, S. A., Fallah, M., & Najafi, S. E. (2022). A multi-objective approach based on Markowitz and DEA cross-efficiency models for the intuitionistic fuzzy portfolio selection problem. Decision Making: Applications in Management and Engineering, 5(2), 241-259. https://doi.org/10.31181/dmame0324062022e.
Ghosal, S. G., Dey, S., Chattopadhyay, P. P., Datta, S., & Bhattacharyya, P. (2021). Designing optimized ternary catalytic alloy electrode for efficiency improvement of semiconductor gas sensors using a machine learning approach. Decision Making: Applications in Management and Engineering, 4(2), 126-139. https://doi.org/10.31181/dmame210402126g.
Negi, G., Kumar, A., Pant, S., & Ram, M. (2021). Optimization of complex system reliability using hybrid grey wolf optimizer. Decision Making: Applications in Management and Engineering, 4(2), 241-256. https://doi.org/10.31181/DMAME210402241N.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2023 Decision Making: Applications in Management and Engineering
This work is licensed under a Creative Commons Attribution 4.0 International License.
