Numerical simulation and optimization for the vibration of the seed metering device based on a novel ML-IGA method

The reported researches only analyze vibration parameters of the seed-metering device. However, they do not adopt any algorithm to conduct a multi-objective optimization for the vibration performance of the seed-metering device. In this paper, the vibration of the seed-metering device is numerically computed firstly, and the correctness of the computational model is validated by experimental test. Then, some parameters including the plate thickness, inclination angle, excitation source position and supporting leg thickness of the seed-metering device are studied. In this way, those parameters which have serious impacts on the vibration are obtained. Therefore, the vibration performance is tried to optimize using these parameters as the design variables. This paper proposes a novel multi-layer immune algorithm based on genetic algorithms (ML-IGA). In order to further verify validity of the novel ML-IGA model for optimizing the vibration of the seed-metering device, it is compared with the traditional GA model and PSO-GA model. When the iteration of ML-IGA model was conducted to the 156th generation, the predicted error is smaller than the set critical error. Compared with other two kinds of algorithms, the optimized time is reduced. Regarding optimization of the GA model, the vibration amplitudes are increased by 2.49 % and 4.22 % respectively. Regarding optimization of the PSO-GA model, the vibration amplitudes are increased by 9.74 % and 6.024 % respectively. Regarding optimization of the ML-IGA model, the vibration amplitudes are increased by 17.34 % and 6.78 % respectively. Obviously, structures with better performance can be obtained through using the novel ML-IGA model.

[1]  Arzu Yazgi,et al.  Measurement of seed spacing uniformity performance of a precision metering unit as function of the number of holes on vacuum plate , 2014 .

[2]  Zhao Yun.  The Modal Analysis and Experimental Study on Vibration Seed-Metering Device , 2011 .

[3]  Li Ge.  Seed metering device based on orienting ordered arrangement in embryo and parameter optimization , 2008 .

[4]  M. A. El-Shorbagy,et al.  Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems , 2011, J. Comput. Appl. Math..

[5]  Yu Yongchang,et al.  Test on Effect of the Operating Speed of Maize-soybean Interplanting Seeders on Performance of Seeder-metering Devices , 2017 .

[6]  Oscar Castillo,et al.  An optimization method for designing type-2 fuzzy inference systems based on the footprint of uncertainty using genetic algorithms , 2012, Expert Syst. Appl..

[7]  Chen Chang-chu.  Simulation and Analysis of Seeding Speed for Vibrating Precision Seed Meter , 2014 .

[8]  Henry C. W. Lau,et al.  Application of Genetic Algorithms to Solve the Multidepot Vehicle Routing Problem , 2010, IEEE Transactions on Automation Science and Engineering.

[9]  Zhi-Hua Hu,et al.  A container multimodal transportation scheduling approach based on immune affinity model for emergency relief , 2011, Expert Syst. Appl..

[10]  Navid Freidoonimehr,et al.  Parametric analysis and optimization of entropy generation in unsteady MHD flow over a stretching rotating disk using artificial neural network and particle swarm optimization algorithm , 2013 .

[11]  A. A. Abou El-Ela,et al.  Maximal optimal benefits of distributed generation using genetic algorithms , 2010 .

[12]  Chen Chen,et al.  Movement law of maize population in seed room of seed metering device based on discrete element method , 2016 .

[13]  Terry E. Shoup,et al.  A poly-hybrid PSO optimization method with intelligent parameter adjustment , 2011, Adv. Eng. Softw..

[14]  Mehmet Korürek,et al.  ECG beat classification using particle swarm optimization and radial basis function neural network , 2010, Expert Syst. Appl..

[15]  M. Anantachar,et al.  Development of artificial neural network models for the performance prediction of an inclined plate seed metering device , 2011, Appl. Soft Comput..

[16]  Andrew Guzzomi,et al.  Precision metering of Santalum spicatum (Australian Sandalwood) seeds , 2013 .

[17]  Akira Mita,et al.  Hybrid Methodology for Structural Health Monitoring Based on Immune Algorithms and Symbolic Time Series Analysis , 2013 .

[18]  M. Anantachar,et al.  Neural network prediction of performance parameters of an inclined plate seed metering device and its reverse mapping for the determination of optimum design and operational parameters , 2010 .

[19]  Masoud Rabbani,et al.  An Artificial Immune Algorithm for the project scheduling problem under resource constraints , 2011, Appl. Soft Comput..

[20]  S. M. Muyeen,et al.  Design Optimization of Controller Parameters Used in Variable Speed Wind Energy Conversion System by Genetic Algorithms , 2012, IEEE Transactions on Sustainable Energy.

[21]  Chao Chen,et al.  An experimental investigation of two Wavelet-MLP hybrid frameworks for wind speed prediction using GA and PSO optimization , 2013 .

[22]  Zhan Zhao,et al.  Monitoring method of rice seeds mass in vibrating tray for vacuum-panel precision seeder , 2015, Comput. Electron. Agric..