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Robot Manipulator Calibration Using a Model based Identification Technique and a Neural Network With the teaching learning-based optimization

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IEEE ACCESS 2020年 8卷 105447-105454页

作者： Phu-Nguyen Le Kang, Hee-Jun Univ Ulsan Grad Sch Elect Engn Ulsan 680749 South Korea Univ Ulsan Sch Elect Engn Ulsan 680749 South Korea

This paper proposes a new calibration method for enhancing robot positional accuracy of the industrial manipulators. By combining the joint deflection model with the conventional kinematic model of a manipulator, the geometric errors and joint deflection errors can be considered together to increase its positional accuracy. Then, a neural network is designed to additionally compensate the unmodeled errors, specially, non-geometric errors. The teaching-learning-based optimization method is employed to optimize weights and bias of the neural network. In order to demonstrate the effectiveness of the proposed method, real experimental studies are carried out on HH 800 manipulator. The enhanced position accuracy of the manipulator after the calibration confirms the feasibility and more positional accuracy over the other calibration methods.

关键词： Neural network robot accuracy robot calibration teaching-learning-based optimization

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An efficient hybrid approach of improved adaptive neural fuzzy inference system and teaching learning-based optimization for design optimization of a jet pump-based thermoacoustic-Stirling heat engine

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NEURAL COMPUTING & APPLICATIONS 2020年第11期32卷 7259-7273页

作者： Ngoc Le Chau Thanh-Phong Dao Van Anh Dang Ind Univ Ho Chi Minh City Fac Mech Engn Ho Chi Minh City Vietnam Ton Duc Thang Univ Inst Computat Sci Div Computat Mechatron Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam

The acoustic streaming is a key drawback and eliminates the performance of a jet pump-based thermoacoustic-Stirling heat engine. The present study deals with a new hybrid optimization approach to reduce the acoustic streaming energy. The proposed work is an integration of Taguchi method (TM), adaptive neural fuzzy inference system (ANFIS), and teaching-learning-based optimization (TLBO). The Taguchi method plays three important roles. The first role is to layout the number of experiments. The second role is to identify the most appropriate parameters for ANFIS structure regarding the number of input membership functions (MFs), types of input MFs, optimal learning method, and types of output MFs. In order to determine the optimum parameters for the ANFIS structure, the root-mean-squared error, a performance criterion, is minimized by using the TM. The final role of TM is to optimize the controllable parameters of the TLBO. Subsequently, modeling between geometric parameters and acoustic streaming is established by the built ANFIS structure. Finally, the TLBO is adopted by optimizing the design parameters. The outcomes of study revealed that the acoustic streaming is relatively reduced. based on Wilcoxon signed-rank test and Friedman test, it proves that the effectiveness of the proposed hybrid approach is better to other evolutionary algorithms. The current approach is an efficient optimizer for complex optimization problems.

关键词： Jet pump Acoustic streaming optimization Taguchi method ANFIS teaching-learning-based optimization Statistical analysis

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Improved teaching-learning based optimization algorithm using Lyapunov stability analysis

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第7期13卷 3609-3618页

作者： Farivar, Faezeh Shoorehdeli, Mahdi Aliyari Manthouri, Mohammad Islamic Azad Univ Dept Mechatron & Comp Engn Sci & Res Branch Tehran Iran KN Toosi Univ Technol Dept Mechatron Fac Elect Engn Tehran Iran Shahed Univ Elect & Elect Engn Dept Tehran Iran

teaching-learning-based optimization (TLBO) algorithms is one of the swarm-based optimization search algorithms. It develops based on the teaching-learning procedures at a classroom to solve multi-dimensional and nonlinear problems. In this paper, convergence and stability analysis of TLBO are studied. The stability of individual dynamics is analyzed by Lyapunov stability theorem and the concept of system dynamics. Stability conditions are achieved and utilized for adapting parameters of the TLBO. The TLBO algorithm is modified based on stability analysis. The modified TLBO is compared with the standard TLBO, particle swarm optimization (PSO), real genetic algorithm (RGA), and gravitational search algorithm (GSA). Simulation results confirm the validity and feasibility of the proposed modified TLBO. The appropriate performance is achieved for multi-dimensional and nonlinear standard bench functions.

关键词： Convergence Lyapunov stability teaching-learning-based optimization Swarm intelligence algorithms

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Sampling CAD models via an extended teaching-learning-based optimization technique

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COMPUTER-AIDED DESIGN 2018年 100卷 52-67页

作者： Khan, Shahroz Gunpinar, Erkan Istanbul Tech Univ Istanbul Turkey

The teaching-learning-based optimization (TLBO) algorithm of Rao et al. has been presented in recent years, which is a population-based algorithm and operates on the principle of teaching and learning. This algorithm is based on the influence of a teacher on the quality of learners in a population. In this study, TLBO is extended for constrained and unconstrained CAD model sampling which is called Sampling-TLBO (S-TLBO). Sampling CAD models in the design space can be useful for both designers and customers during the design stage. A good sampling technique should generate CAD models uniformly distributed in the entire design space so that designers or customers can well understand possible design options. To sample N designs in a predefined design space, N sub-populations are first generated each of which consists of separate learners. teaching and learning phases are applied for each sub-population one by one which are based on a cost (fitness) function. Iterations are performed until change in the cost values becomes negligibly small. Teachers of each sub-population are regarded as sampled designs after the application of S-TLBO. For unconstrained design sampling, the cost function favors the generation of space-filling and Latin Hypercube designs. Space-filling is achieved using the Audze and Eglais' technique. For constrained design sampling, a static constraint handling mechanism is utilized to penalize designs that do not satisfy the predefined design constraints. Four CAD models, a yacht hull, a wheel rim and two different wine glasses, are employed to validate the performance of the S-TLBO approach. Sampling is first done for unconstrained design spaces, whereby the models obtained are shown to users in order to learn their preferences which are represented in the form of geometric constraints. Samples in constrained design spaces are then generated. According to the experiments in this study, S-TLBO outperforms state-of-the-art techniques particularly when

关键词： teaching-learning-based optimization Design sampling Computer-aided design Generative design

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Spatial-spectral semisupervised classification based on teaching-learning-based optimization for hyperspectral image

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JOURNAL OF APPLIED REMOTE SENSING 2018年第2期12卷

作者： Zhang, Chaozhu Xu, Hongyi Li, Yang Harbin Engn Univ Sch Informat & Commun Engn Harbin Heilongjiang Peoples R China Beijing Inst Space Mech & Elect Image Proc Ctr Beijing Peoples R China

For hyperspectral images, most traditional semisupervised classification algorithms ignore spatial information. An effective hyperspectral spatial-spectral semisupervised classification algorithm based on teaching-learning-based optimization (S3C-TLBO) is proposed. In the algorithm, two aspects were used to expand the training labeled set: first, the spatial neigh-borhood samples of the existing labeled samples with high confidence were labeled to expand the labeled training samples. Second, the margin sampling (MS) combined with improved TLBO was exploited to quickly select the unlabeled samples near the classification hyperplanes from the unlabeled sample set. In addition, spatial neighborhood information was incorporated into kernel function to train a spatial-spectral support vector machine, which was used to determine the category of the test sample comprehensively. The image noise points were removed by smoothing the classification result with neighborhood information. Two sets of experiments on two hyperspectral datasets showed that the proposed algorithm can produce greater classification accuracy and reduce noise points by organically using the combinative spatial information. In particular, the S3C-TLBO can produce better classification result in few labeled samples, which is a major difficulty in hyperspectral image classification. (C) 2018 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： hyperspectral image semisupervised classification spatial information spectral information teaching-learning-based optimization

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Multi-Objective Individualized-Instruction teaching-learning-based optimization Algorithm

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APPLIED SOFT COMPUTING 2018年 62卷 288-314页

作者： Yu, Dong Hong, Jun Zhang, Jinhua Niu, Qingbo Xi An Jiao Tong Univ Inst Adv Mfg & Technol Xian 710049 Shaanxi Peoples R China Luoyang Bearing Sci & Technol Co Ltd Luoyang 471000 Henan Peoples R China

Traditional multi-objective evolutionary algorithms (MOEAs) adopt selection and reproduction operators to find approximate solutions for multi-objective optimization problems (MOPs). The Pareto-dominancebased method is an important branch of MOEA research which exploits dominance relations information. To use dominance relations information more efficiently, this paper proposes an individualized instruction mechanism combined with the non-dominated sorting concept and the teaching-learning process of teaching-learning-based optimization (TLBO). This algorithm, with its individualized instruction mechanism (INM-TLBO), places greater emphasis on the guiding role of the non-dominated solution. INM-TLBO designates specific teachers or interactive objects to help learners improve in the individualized teaching learning process and adopts an external archive to preserve the best solution found. In addition, the INM-TLBO needs only generic control parameters as input, such as population size, an epsilon value for the external archive, and a stop criterion (maximal generation or function evaluation). The performance of INM-TLBO was evaluated on three test problem sets, including twelve extensively used unconstrained test problems, six truly disconnected test problems, and ten complex continuous unconstrained optimization test problems originally proposed for the Congress on Evolutionary Computation 2009 (CEC 2009) competition. The numerical results are compared with those of other state-of-the-art algorithms and show that INM-TLBO has good convergence and high robustness on these test problems. (C) 2017 Elsevier B.V. All rights reserved.

关键词： teaching-learning-based optimization Non-dominated sorting Multi-objective optimization epsilon-dominance

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A developed design optimization model for semi-rigid steel frames using teaching-learning-based optimization and genetic algorithms

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STRUCTURAL ENGINEERING AND MECHANICS 2018年第2期66卷 173-183页

作者： Shallan, Osman Maaly, Hassan M. Hamdy, Osman Univ Zagazig Dept Struct Engn Zagazig Egypt

This paper proposes a developed optimization model for steel frames with semi-rigid beam-to-column connections and fixed bases using teaching-learning-based optimization (TLBO) and genetic algorithm (GA) techniques. This method uses rotational deformations of frame members ends as an optimization variable to simultaneously obtain the optimum cross-sections and the most suitable beam-to-column connection type. The total cost of members plus connections cost of the frame are minimized. Frye and Morris (1975) polynomial model is used for modeling nonlinearity of semi-rigid connections, and the P-Delta effect and geometric nonlinearity are considered through a stepped analysis process. The stress and displacement constraints of AISC-LRFD (2016) specifications, along with size fitting constraints, are considered in the design procedure. The developed model is applied to three benchmark steel frames, and the results are compared with previous literature results. The comparisons show that developed model using both LTBO and GA achieves better results than previous approaches in the literature.

关键词： teaching-learning-based optimization genetic algorithm steel frame optimization semi-rigid connections geometrically nonlinear the P-Delta effect rotational deformations variable

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Energy efficient teaching-learning-based optimization for the discrete routing problem in wireless sensor networks

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APPLIED INTELLIGENCE 2018年第9期48卷 2755-2769页

作者： El Ghazi, Asmae Ahiod, Belaid Mohammed V Univ Rabat LRIT Associated Unit CNRST URAC 29 Fac Sci Rabat Morocco

Wireless sensor networks (WSNs) are composed of sensor nodes, having limited energy resources and low processing capability. Accordingly, major challenges are involved in WSNs Routing. Thus, in many use cases, routing is considered as an NP-hard optimization problem. Many routing protocols are based on metaheuristics, such as Ant Colony optimization (ACO) and Particle Swarm optimization (PSO). Despite the fact that metaheuristics have provided elegant solutions, they still suffer from complexity concerns and difficulty of parameter tuning. In this paper, we propose a new routing approach based on teaching learning based optimization (TLBO) which is a recent and robust method, consisting on two essential phases: Teacher and Learner. As TLBO was proposed for continuous optimization problems, this work presents the first use of TLBO for the discrete problem of WSN routing. The approach is well founded theoretically as well as detailed algorithmically. Experimental results show that our approach allows obtaining lower energy consumption which leads to a better WSN lifetime. Our method is also compared to some typical routing methods;PSO approach, advanced ACO approach, Improved Harmony based approach (IHSBEER) and Ad-hoc On-demand Distance Vector (AODV) routing protocol, to illustrate TLBO's routing efficiency.

关键词： Wireless sensor network Metaheuristic Routing Ad-hoc on-demand distance vector Ant colony optimization Particle swarm optimization Harmony search teaching-learning-based optimization

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teaching-learning-based optimization algorithm for multi-skill resource constrained project scheduling problem

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SOFT COMPUTING 2017年第6期21卷 1537-1548页

作者： Zheng, Huan-yu Wang, Ling Zheng, Xiao-long Tsinghua Univ Dept Automat Tsinghua Natl Lab Informat Sci & Technol TNList Beijing 100084 Peoples R China

In this paper, a teaching-learning-based optimization algorithm (TLBO) is proposed to solve the multi-skill resource constrained project scheduling problem (MS-RCPSP) with makespan minimization criterion. A task-resource list-based encoding scheme is presented by combining the task list and the resource list, and a left-shift decoding scheme is developed to generate feasible schedules. To achieve satisfactory performances, the balance between global exploration and local exploitation is stressed in designing the TLBO algorithm. At the initialization stage, a balanced resource rule is proposed to generate the initial resource lists, and multiple task list rules are adopted in a hybrid way to initialize the task lists. At the teacher phase and the student phase, the two-point crossover and the resource-based local search are utilized to generate the promising task-resource lists. Moreover, a reinforcement phase is incorporated into the original TLBO with both the permutation-based and the resource-based local search strategies as an additional phase to enhance the local intensification capability. To investigate the influence of parameter setting on the TLBO, numerical tests based on Taguchi method of design of experiment are carried out. In addition, the effectiveness of the proposed balanced resource rule is shown by statistical comparisons with the random resource rule. Computational comparisons between TLBO and the existing algorithm also demonstrate the effectiveness and efficiency of the proposed TLBO in solving the MS-RCPSP.

关键词： teaching-learning-based optimization Project scheduling Multi-skill Balanced resource rule Reinforcement phase

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Modified Energy-Efficient Range-Free Localization Using teaching-learning-based optimization for Wireless Sensor Networks

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IETE JOURNAL OF RESEARCH 2018年第1期64卷 124-138页

作者： Sharma, Gaurav Kumar, Ashok Natl Inst Technol Dept Elect & Commun Engn Hamirpur Himachal Prades India

This paper presents an energy-efficient Modified Distance Vector Hop algorithm using teaching-learning-based optimization, viz. MDV-TLBO, which is range-free, distributed localization algorithm for wireless sensor network (WSN). In the proposed algorithm, the hop size of anchor node is modified by adding correction factor. The concept of collinearity is introduced in this paper to reduce location errors caused by anchor nodes which are collinear. TLBO is used to enhance the localization accuracy, which is parameter-free, efficient optimization technique. Target nodes estimate their final coordinates after location upgradation procedure. In MDV-TLBO, anchor nodes communicate only one time with target nodes to broadcast their location. Hop size modification, optimal selection of anchor nodes, location optimization, and location upgradation are done at target node level, resulting considerable reduction in communication between nodes, due to which energy consumption of the nodes has been significantly reduced. To show the applicability of proposed algorithm in real scenarios, radio irregularity model is considered. Simulation results show that our proposed algorithm achieves better localization accuracy, high positioning coverage with less energy consumption in comparison of the existing improved DV-Hop algorithms.

关键词： DV-Hop algorithm Localization optimization teaching-learning-based optimization Wireless sensor networks

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