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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| Vydáno v: | Microsystem technologies : sensors, actuators, systems integration Ročník 25; číslo 5; s. 2067 - 2083 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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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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| Title | Multi-objective optimization design of a compliant microgripper based on hybrid teaching learning-based optimization algorithm |
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