Abstract
In order to optimize the design of gear reducer, gear reducer optimal design to improve reliability and security, slow convergence and local optimum for FOA algorithm is proposed based on the improved type FOA gear reducer optimization design model. To avoid falling into local optimum Drosophila optimization algorithms, improved FOA algorithm by introducing a correction factor. Superior to the gear reducer seven variables optimization design model for the study, to ensure the safety and reliability of the premise, the FOA has the advantage of improved convergence speed and avoid local optimization problems, in order to verify the proposed method and reliability.
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References
He, B., Lxian, C., Csheng, L.: Optimized design of gear transmission based on the shuffled frog leaping algorithm. J. Mech. Transm. 7, 11–15 (2013)
Pan, W.T.: A new fruit fly optimization algorithm: taking the financial distress model as an example. Knowl.-Based Syst. 26, 69–74 (2012)
Lxian, C., Zhong, C.: Hybrid discrete differential evolution with a self-adaptive penalty function for constrained engineering optimization. J. Mech. Eng. 3, 21–25 (2011)
Wang, Q., Zhang, W.P., Shi, L.: Improved genetic algorithms’ application in reducer optimization design. Mech. Res. Appl. 2, 18–24 (2009)
Hyun, L., Fkai, K.: Planetary gear optimization based on improved genetic algorithm. Appl. Sci. Technol. 12, 7–12 (2009)
Wu, T., Zhang, L.B., Huang, L.: Gear reducer optimal design based on genetic algorithm. Coal Mine Mach. 12, 17–22 (2009)
Hxia, D.: Optimal design of bevel gear reducer based on genetic algorithm. Mod. Manuf. Technol. Equip. 4, 35–41 (2010)
Luo, X.H., Zhang, R.H., Cao, K., Shi, Y.S.: An improved genetic algorithm and its application in optimization design of the gear transmissions. Mach. Des. Res. 2, 17–20 (2006)
Jwei, W., Jming, Z., Xpeng, W.: Multi-objective optimization design of gear reducer based on simulated annealing algorithms. Trans. Chin. Soc. Agric. Mach. 10, 5–11 (2006)
Li, X.Y., Wang, C.X., Guo, Z.Q., Yi, N.: An improved genetic algorithm and its application in engineering optimization. J. Inner Mongolia Univ. Sci. Technol. 3, 9–12 (2007)
Jjun, Y., Hong, Z., Xguo, C.: Optimized design of gear based on hybrid genetic algorithm. Hoisting Conveying Mach. 9, 12–16 (2008)
Gao, Y.G., Wang, G.B., Ding, Y.Z.: Optimized design of helical gear reducer based on genetic algorithm. Hoisting Conveying Mach. 8, 33–37 (2003)
Che, L.X.: Study on differential evolution algorithms orientating analysis and design of mechanisms. J. Chin. Univ. Min. Technol. (2013)
Shi, X.: Optimal design of gear based on improved genetic algorithm and its three-dimensional papametric modeing technology. Qingdao University of Technology (2013)
Acknowledgement
The work of Fuquan Zhang was supported by the Scientific Research Foundation of Minjiang University (No. MYK17021).
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Lin, X., Zhang, F., Xu, L. (2018). Design of Gear Reducer Based on FOA Optimization Algorithm. In: Pan, JS., Wu, TY., Zhao, Y., Jain, L. (eds) Advances in Smart Vehicular Technology, Transportation, Communication and Applications. VTCA 2017. Smart Innovation, Systems and Technologies, vol 86. Springer, Cham. https://doi.org/10.1007/978-3-319-70730-3_29
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DOI: https://doi.org/10.1007/978-3-319-70730-3_29
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