Multi-objective optimization design of wheat centralized seed feeding device based on particle swarm optimization (PSO) algorithm
Keywords:
centralized seed feeding device, multi-objective, optimization, PSO algorithmAbstract
In order to solve the problem of interaction between multiple evaluation indexes of seed metering performance under multiple factors of centralized seed feeding device, a multi-objective optimization of structure based on particle swarm optimization (PSO) algorithm was proposed in this paper. The wheat centralized seed feeding device was taken as the research object, and the experimental factors were cone angle of type hole, working speed and seed filling gap. The working process of wheat centralized seed feeding device was simulated by discrete element method (DEM). The average seed number of type hole, the variation coefficient of the average seed number of type hole, and the maximum tangential force between seed and seed feeding mechanism were selected as the evaluation indexes. Through the variance analysis of the evaluation indexes by the experimental factors, the optimization objective function was constructed. Using PSO algorithm, the multi-objective optimization was carried out for the wheat centralized seed feeding device. The optimization results show that the best structural combination parameters of the wheat centralized seed feeding device are the hole cone angle of 31.6° and the seed filling gap of 4.6 mm. The validity of the method was verified by simulation and field test. The results show that the PSO algorithm multi-objective optimization method proposed in this paper can provide a reference for the structural improvement and optimal design of the centralized seed feeding device. Keywords: centralized seed feeding device, multi-objective, optimization, PSO algorithm DOI: 10.25165/j.ijabe.20201306.5665 Citation: Wang Q Q, Li Z D, Wang W W, Zhang C L, Chen L Q, Wan L. Multi-objective optimization design of wheat centralized seed feeding device based on particle swarm optimization (PSO) algorithm. Int J Agric & Biol Eng, 2020; 13(6): 76–84.References
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[7] Lei X L, Liao Y T, Liao Q X. Simulation of seed motion in seed feeding device with DEM-CFD coupling approach for rapeseed and wheat. Computers and Electronics in Agriculture, 2016; 131: 29–39.
[8] Lei X L, Liao Y T, Zhang Q S, Wang L, Liao Q X. Numerical simulation of seed motion characteristics of distribution head for rapeseed and wheat. Computers and Electronics in Agriculture, 2018; 150: 98–109.
[9] Liao Y T, Wang L, Liao Q X. Design and test of an inside-filling pneumatic precision centralized seed-metering device for rapeseed. Int J Agric & Biol Eng, 2017; 10(2): 56–62.
[10] Zhang G Z, Zang Y, Luo X W, Wang Z M, Zhang Q, Zhang S S. Design and indoor simulated experiment of pneumatic rice seed metering device. Int J Agric & Biol Eng, 2015; 8(4): 10–18.
[11] Han D D, Zhang D X, Jing H R, Yang L, Cui T, Ding Y Q, et al. DEM-CFD coupling simulation and optimization of an inside-fifilling airblowing maize precision seed-metering device. Computers and Electronics in Agriculture, 2018; 150: 426–438.
[12] Liu J X, Wang Q J, Li H W, He J, Lu C Y. Design and seed suction performance of pinhole-tube wheat precision seeding device. Transactions of the CSAE, 2019; 35(11): 10–18.
[13] Zhou X Y, Shi Y J, Zhu J, Zhao L B, Zhu Z S. Structural multi-objective optimization on a MUAV-based pan–tilt for aerial remote sensing applications. ISA Transactions, 2019; 11: 036.
[14] Hrvoje D, Tomislav P, Neven D. Analysis of displacing natural gas boiler units in district heating systems by using multi-objective optimization and different taxing approaches. Energy Conversion and Management, 2010; 205: 112411.
[15] Paul R W, Nathan R M, Matthew J M. The use of multi-objective optimization to improve the design process of nuclear power plant systems. Annals of Nuclear Energy, 2020; 137: 107079.
[16] Kihan K, Minsik S, Seungjae M. Efficient multi-objective optimization of gear ratios and motor torque distribution for electric vehicles with two-motor and two-speed powertrain system. Applied Energy, 2019; 259: 114190.
[17] Cai X W, Gao L, Li F. Sequential approximation optimization assisted particle swarm optimization for expensive problems. Applied Soft
Computing Journal, 2019; 83: 105659.
[18] Ding Y M, Zhang Y, Zhang J Q, Zhou R, Ren Z Y, Guo H L. Kinetic parameters estimation of pinus sylvestris pyrolysis by Kissinger-Kai method coupled with Particle Swarm Optimization and global sensitivity analysis. Bioresource Technology, 2019; 293: 122079.
[19] Harrison K R, Ombuki-Berman B M, Engelbrecht A P. A parameter-free particle swarm optimization algorithm using performance classifiers. Information Sciences, 2019; 503: 381–400.
[20] Li Z D, Wang Q Q, Zhang Y L, Wang W W, Yang Y, Chen L Q. Design and test of inclined parabola-type hole wheel wheat seed feeder. Transactions of the CSAM, 2018; 49: 116–124.
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Published
2020-12-03
How to Cite
Wang, Q., Li, Z., Wang, W., Zhang, C., Chen, L., & Wan, L. (2020). Multi-objective optimization design of wheat centralized seed feeding device based on particle swarm optimization (PSO) algorithm. International Journal of Agricultural and Biological Engineering, 13(6), 76–84. Retrieved from https://ijabe.migration.pkpps03.publicknowledgeproject.org/index.php/ijabe/article/view/5665
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Power and Machinery Systems
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