MULTI-OBJECTIVE OPTIMIZATION OF TWO-STAGE HELICAL GEARBOXES USING NSGA-II AND MCDM METHODS: MINIMIZING MASS AND MAXIMIZING EFFICIENCY
Abstract
This study presents a multi-criteria design approach for two-stage helical gearboxes using the Non-dominated Sorting Genetic Algorithm II (NSGA-II) in combination with MCDM methods. The optimization problem was formulated with two conflicting objectives: minimizing gearbox mass and maximizing transmission efficiency. NSGA-II was employed to generate a set of Pareto-optimal solutions, while three different MCDM methods—MARCOS (the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS), TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), and SAW (Simple Additive Weighting)— was applied to identify the most preferred compromise design among them. Regression analysis revealed a strong linear relationship between the stage-one gear ratio (u1) and the overall transmission ratio (uh), ensuring practical feasibility of the optimized gear allocation. Numerical results demonstrated that gearboxes designed with transmission ratios in the range of uh=15–25 achieved the most balanced trade-off between reduced mass and high efficiency. The proposed hybrid NSGA-II–MCDM framework thus provides a powerful decision-support tool for gearbox designers, enabling both lightweight and energy-efficient configurations. In addition, the findings have clear practical implications, as the proposed framework can support the development of more compact and efficient gearboxes in automotive and industrial applications, where reducing mass and improving efficiency are critical design requirements.
References
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197. https://doi.org/10.1109/4235.996017
Konak, A., Coit, D. W., & Smith, A. E. (2006). Multi-objective optimization using genetic algorithms: A tutorial. Reliability Engineering & System Safety, 91(9), 992–1007. https://doi.org/10.1016/j.ress.2005.11.018
Savsani, V., Rao, R. V., & Vakharia, D. (2010). Optimal weight design of a gear train using particle swarm optimization and simulated annealing algorithms. Mechanism and Machine Theory, 45(3), 531–541. https://doi.org/10.1016/j.mechmachtheory.2009.10.010
Patil, M., Ramkumar, P., & Shankar, K. (2017). Multi-objective optimization of spur gearbox with inclusion of tribological aspects. Journal of Friction and Wear, 38(5), 430–436. https://doi.org/10.3103/S1068366617060101
Le, X.-H., & Vu, N.-P. (2023). Multi-objective optimization of a two-stage helical gearbox using Taguchi method and grey relational analysis. Applied Sciences, 13(13), 760. https://doi.org/10.3390/app13137601
Tran, H.-D., Dinh, V.-T., Vu, D.-B., Vu, D., Luu, A.-T., & Vu, N.-P. (2024). Application of the TOPSIS method for multi-objective optimization of a two-stage helical gearbox. Engineering, Technology & Applied Science Research, 14(4), 15454–15463. https://doi.org/10.48084/etasr.7551
Sanghvi, R., Vashi, A., Patolia, H., & Jivani, R. (2014). Multi-objective optimization of two-stage helical gear train using NSGA-II. Journal of Optimization, 2014, 670297. https://doi.org/10.1155/2014/670297
Patil, M., Ramkumar, P., & Krishnapillai, S. (2017). Multi-objective optimization of two-stage spur gearbox using NSGA-II. SAE Technical Paper. https://doi.org/10.4271/2017-28-1939
Maputi, E. S., & Arora, R. (2020). Multi-objective optimization of a 2-stage spur gearbox using NSGA-II and decision-making methods. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 42, 1–22. https://doi.org/10.1007/s40430-020-02557-2
Ananthapadmanabhan, R., Babu, S. A., Hareendranath, K., Krishnamohan, C., & Krishnapillai, S. (2016). Investigation on multiple algorithms for multi-objective optimization of gearbox. IOP Conference Series: Materials Science and Engineering, 149, 012049. https://doi.org/10.1088/1757-899X/149/1/012049
Méndez, M., Rossit, D. A., González, B., & Frutos, M. (2020). Proposal and comparative study of evolutionary algorithms for optimum design of a gear system. IEEE Access, 8, 3482–3497. https://doi.org/10.1109/ACCESS.2019.2962906
Lei, Y., Hou, L., Fu, Y., Hu, J., & Chen, W. (2020). Research on vibration and noise reduction of electric bus gearbox based on multi-objective optimization. Applied Acoustics, 158, 107037. https://doi.org/10.1016/j.apacoust.2019.107037
Qi, L., Zhou, J., & Xu, H. (2022). Multi-objective optimization of gearbox based on panel acoustic participation and response surface methodology. Journal of Low Frequency Noise, Vibration and Active Control, 41(3), 1108–1130. https://doi.org/10.1177/14613484221091075
Younes, E. B., Changenet, C., Bruyère, J., Rigaud, E., & Perret-Liaudet, J. (2022). Multi-objective optimization of gear unit design to improve efficiency and transmission error. Mechanism and Machine Theory, 167, 104499. https://doi.org/10.1016/j.mechmachtheory.2021.104499
Hu, M., Zhu, J., Gong, L., Lu, Z., & Liu, H. (2025). Multi-objective optimization of an aeroengine accessory gearbox transmission based on a heuristic algorithm. Journal of Aerospace Engineering, 38(2), 04024126. https://doi.org/10.1061/JAEEEZ.ASENG-5155
Binh, V. D., Van Thanh, D., Nguyen, K. M., & Hung, L. X. (2024). Multi-objective optimization of a two-stage helical gearbox using MARCOS. Engineering, Technology & Applied Science Research, 14(6), 18245–18251. https://doi.org/10.48084/etasr.8865
Karim, M. A., Abdullah, M. Z., Deifalla, A. F., Azab, M., & Waqar, A. (2023). An assessment of fibre-reinforced polymers (FRPs) in petroleum and natural gas industries: A review. Results in Engineering, 18, 101091. https://doi.org/10.1016/j.rineng.2023.101091
Karim, M. A., Abdullah, M. Z., Waqar, A., Deifalla, A. F., Ragab, A. E., & Khan, M. (2023). Mechanical properties of single-layered braid reinforced thermoplastic pipe (BRTP). Results in Engineering, 20, 101483. https://doi.org/10.1016/j.rineng.2023.101483
Umurani, K., Karim, M. A., Abdullah, C., & Tanjung, L. E. (2025). Manufacturing techniques for fiber reinforced polymer composites. Journal of Advanced Research in Micro and Nano Engineering, 41(1), 84–124. https://doi.org/10.37934/armne.41.1.84124
Harahap, P., et al. (2024). Power conversion from solar panels using a 3000-watt inverter. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 124(2), 260–272. https://doi.org/10.37934/arfmts.124.2.260272
Karim, M. A. (2024). Performance measurement of Peltier element design using solar test simulator. Journal of Advanced Research in Fluid Mechanics and Thermal Sciences, 123(2), 231–243. https://doi.org/10.37934/arfmts.123.2.231243
Mankhi, T. A., Legutko, S., Al-Bedhany, J. H., & Muhsen, A. A. (2019). Selecting the most efficient bearing of wind turbine gearbox using AHP. IOP Conference Series: Materials Science and Engineering, 518, 032050. https://doi.org/10.1088/1757-899X/518/3/032050
Terán, C. V., Martínez-Gómez, J., & López Milla, J. C. (2020). Material selection through multi-criteria decision methods applied to a helical gearbox. International Journal of Mathematics in Operational Research, 17(1), 90–109. https://doi.org/10.1504/IJMOR.2020.109035
Yan, K., Gao, P., Wu, Z., Liu, H., & Xiang, C. (2025). Multi-criteria decision-making analysis for planetary gear systems. IEEE Sensors Journal. https://doi.org/10.1109/JSEN.2025.3563581
Jovanović, J., et al. (2025). Optimization of gear pairs in a two-stage planetary gearbox using AHP and TOPSIS. Communications – Scientific Letters of the University of Žilina, 27(1), B65–B74. https://doi.org/10.26552/com.C.2025.014
Chat, T., & Van Uyen, L. (2007). Design and calculation of mechanical transmission systems (Vol. 1). Educational Publishing House.
Jelaska, D. T. (2012). Gears and gear drives. John Wiley & Sons.
Stević, Ž., Pamučar, D., Puška, A., & Chatterjee, P. (2020). Sustainable supplier selection using MARCOS method. Computers & Industrial Engineering, 140, 106231. https://doi.org/10.1016/j.cie.2019.106231
Hwang, C.-L., Lai, Y.-J., & Liu, T.-Y. (1993). A new approach for multiple objective decision making. Computers & Operations Research, 20(8), 889–899. https://doi.org/10.1016/0305-0548(93)90109-V
Sri, K., Hartati, S., Harjoko, A., & Wardoyo, R. (2006). Fuzzy multi-attribute decision making (Fuzzy MADM). Graha Ilmu.
Kumar, R., et al. (2021). Entropy weights method for multi-objective optimization in machining. Journal of Materials Research and Technology, 10, 1471–1492. https://doi.org/10.1016/j.jmrt.2020.12.114
