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Optimal dynamic design of planar mechanisms using teaching-learning-based optimization algorithm

PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2016年第19期230卷 3442-3456页

作者： Chaudhary, Kailash Chaudhary, Himanshu Malaviya Natl Inst Technol Jaipur Dept Mech Engn Jaipur Rajasthan India

A two-stage optimization method for optimal dynamic design of planar mechanisms is presented in this paper. For dynamic balancing, minimization of the shaking force and the shaking moment is achieved by finding optimum mass distribution of mechanism links using the equimomental system of point-masses in the first stage of the optimization. In the second stage, their shapes are synthesized systematically by closed parametric curve, i.e. cubic B-spline curve corresponding to the optimum inertial parameters found in the first stage. The multi-objective optimization problem to minimize both the shaking force and the shaking moment is solved using evolutionary optimization algorithm - "teaching-learning-based optimization (TLBO) algorithm''. The computational performance of TLBO algorithm is compared with another evolutionary optimization algorithm, i.e. genetic algorithm.

关键词： Dynamic balancing equimomental system dynamic design planar mechanism teaching-learning-based optimization algorithm

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Estimating Parameters of Van Genuchten Equation based on teaching-learning-based optimization algorithm 7th

Estimating Parameters of Van Genuchten Equation Based on Tea...

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7th International Symposium on Intelligence Computation and Applications (ISICA)

作者： Gu, Fahui Li, Kangshun Yang, Lei Li, Wei South China Agr Univ Sch Math & Informat Guangzhou 510006 Guangdong Peoples R China Jiangxi Appl Technol Vocat Coll Sch Informat Engn Ganzhou 341000 Jiangxi Peoples R China

ISBN: (纸本)9789811003561;9789811003554

The Van Genuchten Equation (VGE) is used to describe the characteristic of soil water movement, but it is super-set, nonlinear and containing many unknown parameters. Using the traditional method to estimate the parameters of VGE often results in a high margin of error because of complication. The teaching-learning-based optimization (TLBO) is a new swarm intelligent optimization method for solving complex nonlinear models. In this paper, the solution program of TLBO is compiled and used to estimate parameters of the VGE. The results show that the estimate method by TLBO is more efficient and accurate. Consequently, TLBO can be used as a new method to estimate parameters of VGE.

关键词： Van Genuchten Equation (VGE) teaching-learning-based optimization algorithm Estimating parameters Soil water retention curve

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Effective Hybrid teaching-learning-based optimization algorithm for Balancing Two-sided Assembly Lines with Multiple Constraints

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Chinese Journal of Mechanical Engineering 2015年第5期28卷 1067-1079页

作者： TANG Qiuhua LI Zixiang ZHANG Liping FLOUDAS C A CAO Xiaojun Industrial Engineering Department Wuhan University of Science and Technology Texas A&M Energy Institute Texas A&M University College Station Technique Center of Dongfeng Peugeot Citroen Automobile Company

Due to the NP-hardness of the two-sided assembly line balancing （TALB） problem, multiple constraints existing in real applications are less studied, especially when one task is involved with several constraints. In this paper, an effective hybrid algorithm is proposed to address the TALB problem with multiple constraints （TALB-MC）. Considering the discrete attribute of TALB-MC and the continuous attribute of the standard teaching-learning-based optimization （TLBO） algorithm, the random-keys method is hired in task permutation representation, for the purpose of bridging the gap between them. Subsequently, a special mechanism for handling multiple constraints is developed. In the mechanism, the directions constraint of each task is ensured by the direction check and adjustment. The zoning constraints and the synchronism constraints are satisfied by teasing out the hidden correlations among constraints. The positional constraint is allowed to be violated to some extent in decoding and punished in cost fimction. Finally, with the TLBO seeking for the global optimum, the variable neighborhood search （VNS） is further hybridized to extend the local search space. The experimental results show that the proposed hybrid algorithm outperforms the late acceptance hill-climbing algorithm （LAHC） for TALB-MC in most cases, especially for large-size problems with multiple constraints, and demonstrates well balance between the exploration and the exploitation. This research proposes an effective and efficient algorithm for solving TALB-MC problem by hybridizing the TLBO and VNS.

关键词： two-sided assembly line balancing teaching-learning-based optimization algorithm variable neighborhood search positional constraints zoning constraints synchronism constraints

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Design optimization of robot grippers using teaching-learning-based optimization algorithm

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ADVANCED ROBOTICS 2015年第6期29卷 431-447页

作者： Rao, R. Venkata Waghmare, Gajanan SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

This paper presents the performance of teaching-learning-based optimization (TLBO) algorithm to obtain the optimum geometrical dimensions of a robot gripper. Design of robot grippers is an important aspect of the overall robot performance and the most demanding process in any robot system to match the need for the production requirement. Robot grippers are the end effectors used to grasp and hold the objects. A gripper acts as the bridge between the robot arm and the environment around it. Five objectives, namely the difference between the maximum and minimum gripping forces, the force transmission ratio, the shift transmission ratio, length of all the elements of the gripper, and the effort of the gripper mechanism are considered as objective functions. The problem has seven design variables, including six variables as dimensions of the gripper and one variable as the angle between two links of the gripper. Three robot grippers are optimized. Computational results shows that the TLBO algorithm is better or competitive to other optimization algorithms recently reported in the literature for the considered problem.

关键词： teaching-learning-based optimization algorithm design optimization robot gripper

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Selection of laser bending process parameters for maximal deformation angle through neural network and teaching-learning-based optimization algorithm

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SOFT COMPUTING 2015年第3期19卷 609-620页

作者： Omidvar, Mahyar Fard, Reza Kashiry Sohrabpoor, Hamed Teimouri, Reza Golpayegan Shohada Univ Engn & Technol Golpayegan Iran Islamic Azad Univ Kordestan Branch Sci & Res Dept Mech Engn Sanandaj Iran Islamic Azad Univ Dezfull Branch Dept Mech Engn Dezful Iran Babol Univ Technol Dept Mech Engn Babol Sar Iran

The present study focused on selecting optimal factors combination that causes maximum bending angle in laser bending of AA6061-T6. For this purpose a L-25 Taguchi orthogonal design (four factors-five levels) is used to design experiments. Here, the process main factors are laser power, spot diameter, pulse duration and scanning speed and the main response was bending angle. To correlate relationship between process factors and bending angle, a radial basis function neural network (RBFNN) was utilized. Then the developed RBFNN model was used as an objective function for maximizing deformation through teaching-learning-based optimization algorithm. Results indicated that the laser power of 3.6kW, spot diameter of 2 mm, pulse duration of 0.9 ms and scanning speed of 2 mm/s lead to maximal bending angle about 28.7 degrees. Hereafter the optimal results have been verified by confirmatory experiments.

关键词： Laser bending L-25 Taguchi design Radial basis network teaching-learning-based optimization algorithm

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Multi-objective design optimization of a plate-fin heat sink using a teaching-learning-based optimization algorithm

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APPLIED THERMAL ENGINEERING 2015年 76卷 521-529页

作者： Rao, R. V. Waghmare, G. G. SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

This paper presents the multiobjective design optimization of a plate-fin heat sink equipped with flow-through and impingement-flow air cooling systems using teaching-learning-based optimization algorithm. Two objective functions known as entropy generation rate and material cost with five constraints have been taken to measure the performance of the heat sink. Number of fins, height of fins, spacing between two fins and oncoming air velocity are considered as the design variables. The dynamic heat dissipation performance of plate-fin heat sink is investigated using finite element software ANSYS 12.1. The results show the better or competitive performance of the TLBO algorithm over the other optimization algorithms considered by the previous researchers for the same problem. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Multi-objective design optimization Plate-fin heat sink teaching-learning-based optimization algorithm

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Optimal design of planar slider-crank mechanism using teaching-learning-based optimization algorithm

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JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY 2015年第12期29卷 5189-5198页

作者： Chaudhary, Kailash Chaudhary, Himanshu Malaviya Natl Inst Technol Jaipur Dept Mech Engn Jaipur Rajasthan India

In this paper, a two stage optimization technique is presented for optimum design of planar slider-crank mechanism. The slidercrank mechanism needs to be dynamically balanced to reduce vibrations and noise in the engine and to improve the vehicle performance. For dynamic balancing, minimization of the shaking force and the shaking moment is achieved by finding optimum mass distribution of crank and connecting rod using the equimomental system of point-masses in the first stage of the optimization. In the second stage, their shapes are synthesized systematically by closed parametric curve, i.e., cubic B-spline curve corresponding to the optimum inertial parameters found in the first stage. The multi-objective optimization problem to minimize both the shaking force and the shaking moment is solved using teaching-learning-based optimization algorithm (TLBO) and its computational performance is compared with Genetic algorithm (GA).

关键词： Dynamic balancing Equimomental system Link shape optimization Slider-crank mechanism teaching-learning-based optimization algorithm

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optimization of thermal performance of a smooth flat-plate solar air heater using teaching-learning-based optimization algorithm

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COGENT ENGINEERING 2015年第1期2卷 1-N.PAG页

作者： Rao, R. Venkata Waghmare, Gajanan SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

This paper presents the performance of teaching-learning-based optimization (TLBO) algorithm to obtain the optimum set of design and operating parameters for a smooth flat plate solar air heater (SFPSAH). The TLBO algorithm is a recently proposed population-based algorithm, which simulates the teaching-learning process of the classroom. Maximization of thermal efficiency is considered as an objective function for the thermal performance of SFPSAH. The number of glass plates, irradiance, and the Reynolds number are considered as the design parameters and wind velocity, tilt angle, ambient temperature, and emissivity of the plate are considered as the operating parameters to obtain the thermal performance of the SFPSAH using the TLBO algorithm. The computational results have shown that the TLBO algorithm is better or competitive to other optimization algorithms recently reported in the literature for the considered problem.

关键词： smooth flat plate solar air heater thermal efficiency teaching-learning-based optimization algorithm

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A teaching-learning-based optimization algorithm for Solving Set Covering Problems 15th

A Teaching-Learning-Based Optimization Algorithm for Solving...

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15th International Conference on Computational Science and Its Applications (ICCSA)

作者： Crawford, Broderick Soto, Ricardo Aballay, Felipe Misra, Sanjay Johnson, Franklin Paredes, Fernando Pontificia Univ Catolica Valparaiso Valparaiso Chile Univ San Sebastian Santiago Chile Univ Cent Chile Santiago Chile Univ Autonoma Chile Santiago Chile Univ Cient Sur Lima Peru Covenant Univ Ota Nigeria Univ Playa Ancha Valparaiso Chile Univ Diego Portales Escuela Ingn Ind Santiago Chile

ISBN: (纸本)9783319214108;9783319214092

The Set Covering Problem (SCP) is a representation of a kind of combinatorial optimization problem which has been applied in several problems in the real world. In this work we used a binary version of teaching-learning-based optimization (TLBO) algorithm to solve SCP, works with two phases known: teacher and learner;emulating the behavior into a classroom. The proposed algorithm has been tested on 65 benchmark instances. The results show that it has the ability to produce solutions competitively.

关键词： Set Covering Problem teaching-learning-based optimization algorithm Combinatorial optimization Metaheuristics

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Optimal design of the heat pipe using TLBO (teaching learning-based optimization) algorithm

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ENERGY 2015年 80卷 535-544页

作者： Rao, R. V. More, K. C. SV Natl Inst Technol Dept Mech Engn Surat 395007 Gujarat India

Heat pipe is a highly efficient and reliable heat transfer component. It is a closed container designed to transfer a large amount of heat in system. Since the heat pipe operates on a closed two-phase cycle, the heat transfer capacity is greater than for solid conductors. Also, the thermal response time is less than with solid conductors. The three major elemental parts of the rotating heat pipe are: a cylindrical evaporator, a truncated cone condenser, and a fixed amount of working fluid. In this paper, a recently proposed new stochastic advanced optimization algorithm called TLBO (teaching-learning-based optimization) algorithm is used for single objective as well as multi-objective design optimization of heat pipe. It is easy to implement, does not make use of derivatives and it can be applied to unconstrained or constrained problems. Two examples of heat pipe are presented in this paper. The results of application of TLBO algorithm for the design optimization of heat pipe are compared with the NPGA (Niched Pareto Genetic algorithm), GEM (Grenade Explosion Method) and GEO (Generalized External optimization). It is found that the TLBO algorithm has produced better results as compared to those obtained by using NPGA, GEM and GEO algorithms. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Heat pipe Multi-objective optimization Micro-grooves teaching-learning-based optimization algorithm

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