Multi-objective optimization design of a compliant microgripper based on hybrid teaching learning-based optimization algorithm
This article develops a new optimization approach for a compliant microgripper based on a hybrid Taguchi-teaching learning-based optimization algorithm (HTLBO). The optimization problem considers three objective functions and six design variables. The Taguchi’s parameter design is used to produce an...
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| Published in: | Microsystem technologies : sensors, actuators, systems integration Vol. 25; no. 5; pp. 2067 - 2083 |
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| Main Authors: | , , , |
| Format: | Journal Article |
| Language: | English |
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Berlin/Heidelberg
Springer Berlin Heidelberg
01.05.2019
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| ISSN: | 0946-7076, 1432-1858 |
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| Abstract | This article develops a new optimization approach for a compliant microgripper based on a hybrid Taguchi-teaching learning-based optimization algorithm (HTLBO). The optimization problem considers three objective functions and six design variables. The Taguchi’s parameter design is used to produce an initial population for the HTLBO. The weight factor for each response is accurately determined based on the analysis of the signal to noise ratio. Three case studies are taken into account as the basic examples of the proposed algorithm. The computational speed of the proposed algorithm is faster than that of the adaptive elitist differential evolution, the particle swarm optimization, and the genetic algorithm. The results found that the optimal responses from the HTLBO are better than those from other algorithms. The results indicated that the optimal displacement is about 1924.15 µm and the optimal frequency is approximately 170.45 Hz. The simulation and experimental validations are in good agreement with the predicted results. The proposed HTLBO can be applied to solve complicated engineering optimization problems. |
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| AbstractList | This article develops a new optimization approach for a compliant microgripper based on a hybrid Taguchi-teaching learning-based optimization algorithm (HTLBO). The optimization problem considers three objective functions and six design variables. The Taguchi’s parameter design is used to produce an initial population for the HTLBO. The weight factor for each response is accurately determined based on the analysis of the signal to noise ratio. Three case studies are taken into account as the basic examples of the proposed algorithm. The computational speed of the proposed algorithm is faster than that of the adaptive elitist differential evolution, the particle swarm optimization, and the genetic algorithm. The results found that the optimal responses from the HTLBO are better than those from other algorithms. The results indicated that the optimal displacement is about 1924.15 µm and the optimal frequency is approximately 170.45 Hz. The simulation and experimental validations are in good agreement with the predicted results. The proposed HTLBO can be applied to solve complicated engineering optimization problems. |
| Author | Ho, Nhat Linh Le Chau, Ngoc Huang, Shyh-Chour Dao, Thanh-Phong |
| Author_xml | – sequence: 1 givenname: Nhat Linh surname: Ho fullname: Ho, Nhat Linh organization: Faculty of Mechanical Engineering, Ho Chi Minh City University of Technology and Education – sequence: 2 givenname: Thanh-Phong orcidid: 0000-0001-9165-4680 surname: Dao fullname: Dao, Thanh-Phong email: daothanhphong@tdtu.edu.vn organization: Division of Computational Mechatronics, Institute for Computational Science, Ton Duc Thang University, Faculty of Electrical and Electronics Engineering, Ton Duc Thang University – sequence: 3 givenname: Ngoc surname: Le Chau fullname: Le Chau, Ngoc organization: Faculty of Mechanical Engineering, Industrial University of Ho Chi Minh City – sequence: 4 givenname: Shyh-Chour surname: Huang fullname: Huang, Shyh-Chour organization: Department of Mechanical Engineering, National Kaohsiung University of Science and Technology |
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| Cites_doi | 10.1007/s00542-016-3048-3 10.1007/s00542-017-3534-2 10.1007/s00500-016-2432-3 10.1007/s00500-015-1845-8 10.1016/j.ins.2011.08.006 10.1016/j.rcim.2011.06.005 10.1016/j.sna.2011.09.030 10.1016/j.cad.2010.12.015 10.1016/j.cherd.2014.02.005 10.1007/s00158-018-1918-2 10.1080/0305215X.2017.1367392 10.1016/j.asoc.2014.11.027 10.1063/1.4922914 10.1088/0964-1726/24/2/025015 10.1115/1.4024728 10.1016/j.rcim.2005.11.013 10.1016/j.matdes.2006.01.008 10.1007/s00500-006-0068-4 10.1007/s12206-017-0228-8 10.1016/j.sna.2010.01.001 10.1177/0954408917719769 10.1016/j.jmsy.2014.12.001 10.1007/s10845-015-1050-8 10.1016/j.ress.2005.11.018 10.1007/s00170-012-4410-y 10.1016/j.swevo.2015.10.011 10.1007/978-981-10-4852-4_14 10.1007/s00500-015-1741-2 10.1016/j.asoc.2017.04.038 10.1088/0960-1317/23/1/015005 10.1007/BF00932634 10.1016/j.advengsoft.2017.09.007 10.1155/2015/292576 10.1007/s00158-018-2027-y 10.1016/j.jksuci.2013.12.004 10.1007/s13369-018-3445-2 10.1007/s00500-015-1866-3 10.1115/mnc2007-21294 10.1007/978-3-319-73150-6_23 10.1155/2018/8546095 |
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| Copyright | Springer-Verlag GmbH Germany, part of Springer Nature 2018 |
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| Title | Multi-objective optimization design of a compliant microgripper based on hybrid teaching learning-based optimization algorithm |
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