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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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An improved teaching-learning-based optimization with neighborhood search for applications of ANN

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NEUROCOMPUTING 2014年 143卷 231-247页

作者： Wang, Lei Zou, Feng Hei, Xinhong Yang, Dongdong Chen, Debao Jiang, Qiaoyong Xian Univ Technol Sch Comp Sci & Engn Xian 710048 Peoples R China Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China

teaching-learning-based optimization (TLBO) algorithm, which simulates the teaching-learning process of the class room, is one of the recently proposed swarm intelligent (SI) algorithms. The performance of TLBO is maintained by the teaching and learning process, but when the learners cannot found a better position than the old one at some successive iteration, the population might be trapped into local optima. In this paper, an improved teaching-learning-based optimization algorithm with neighborhood search (NSTLBO) is presented. In the proposed method, a ring neighborhood topology is introduced into the original TLBO algorithm to maintain the exploration ability of the population. Different than the traditional method to utilize the global information, the mutation of each learner is now restricted within a certain neighboring area so as to fully utilize the whole space and avoid over-congestion around local optima. Moreover, a mutation operation is presented to NSTLBO during the duplicate eliminations in order to maintain the diversity of population. To verify the performance of the proposed algorithm, thirty-two benchmark functions are utilized. Finally, three application problems of artificial neural network are examined. The results in thirty-two benchmark functions and three applications of ANN indicate that the proposed algorithm has shown interesting outcomes. (C) 2014 Elsevier B.V. All rights reserved.

关键词： teaching-learning-based optimization Neighborhood Artificial neural network

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Multifactorial teaching-learning-based optimization with the diversity and triangle cooperation mechanism

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APPLIED INTELLIGENCE 2022年第14期52卷 16512-16531页

作者： Li, Wei Fan, Yaochi Wang, Lei Jiang, Qiaoyong Xu, Qingzheng Xian Univ Technol Sch Comp Sci & Engn Xian Peoples R China Shaanxi Key Lab Network Comp & Secur Technol Xian Peoples R China Natl Univ Def Technol Coll Informat & Commun Xian Peoples R China

Multifactorial optimization (MFO) is a newly developed optimization framework that can be embedded with an evolutionary algorithm to solve multiple optimization tasks simultaneously. To further explore the generality of the MFO framework, this paper first attempts to use teaching-learning-based optimization as a base optimizer for multiple optimization tasks (MFTLBO). However, the quality of the solution obtained by MFTLBO is not satisfactory because of premature convergence. Therefore, an MFTLBO variant named multifactorial teaching-learning-based optimization with the diversity and triangle cooperation mechanism (DTMFTLBO) is proposed to reduce premature convergence. The diversity indicator is used to dynamically adjust the exploration and exploitation. The cooperative teaching strategy based on weight is designed to acquire evolution direction information, which can guide the population toward more promising solution regions. The triangle cooperation strategy is employed to promote the knowledge transfer between different tasks in the learner phase, which can overcome premature convergence. To verify the efficiency of the proposed DTMFTLBO, numerical studies have been conducted with the 9 commonly used single objective benchmark problems of MTO. Experimental results show that the average excellent rate of the DTMFTLBO for 18 tasks is 85.80%, which confirms the competitiveness of our proposed algorithm compared with eight state-of-the-art multitask optimization algorithms.

关键词： Multifactorial optimization teaching-learning-based optimization Diversity indicator Triangle cooperation mechanism

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Optimal trajectory planning for robotic manipulators using improved teaching-learning-based optimization algorithm

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INDUSTRIAL ROBOT-THE INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH AND APPLICATION 2016年第3期43卷 308-316页

作者： Gao, Xueshan Mu, Yu Gao, Yongzhuo Beijing Inst Technol Intelligent Robot Inst Beijing Peoples R China Beijing Inst Technol Sch Mechatron Engn Beijing Peoples R China Harbin Inst Technol State Key Lab Robot & Syst Harbin Peoples R China

Purpose - The purpose of this paper is to propose a method of optimal trajectory planning for robotic manipulators that applies an improved teaching-learning-based optimization (ITLBO) algorithm. Design/methodology/approach - The ITLBO algorithm possesses better ability to escape from the local optimum by integrating the original TLBO with variable neighborhood search. The trajectory of robotic manipulators complying with the kinematical constraints is constructed by fifth-order B-spline curves. The objective function to be minimized is execution time of the trajectory. Findings - Experimental results with a 6-DOF robotic manipulator applied to surface polishing of metallic workpiece verify the effectiveness of the method. Originality/value - The presented ITLBO algorithm is more efficient than the original TLBO algorithm and its variants. It can be applied to any robotic manipulators to generate time-optimal trajectories.

关键词： B-spline Trajectory planning Robotic manipulators teaching-learning-based optimization Variable neighbourhood search

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Hybrid teaching-learning-based optimization for solving engineering and mathematical problems

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2022年第9期44卷 1-31页

作者： Dastan, Mohammadhossein Shojaee, Saeed Hamzehei-Javaran, Saleh Goodarzimehr, Vahid Shahid Bahonar Univ Kerman Dept Civil Engn Kerman Iran

In this work, a new and effective algorithm called hybrid teaching-learning-based optimization (TLBO) and charged system search (CSS) algorithms (HTC) are proposed to solve engineering and mathematical problems. The CSS is inspired by Coulomb and Gauss's electrostatic laws of physics as well as the Newtonian mechanic laws of motion. The TLBO is inspired by the interaction between teacher and student in a classroom. Usually, the TLBO gets trapped in the local optimal due to the lack of a system for measuring the distance between the student and the optimal point. In order to solve this problem, the CSS algorithm, which is based on the electrical physics laws, is utilized. In the CSS algorithm, each factor is stored under the influence of the best local and global positions, and it is used in subsequent iterations as the possible optimal answers. In fact, this leads to a better balance between exploration and exploitation. In order to validate the proposed method, the CEC2021 and CEC2005 mathematical functions are optimized. Additionally, to show the applicability of the proposed algorithm and to evaluate its performance and convergence rate, several benchmark truss structures are optimized. The weight of the structural elements is taken into account as the objective function, which is optimized under displacement and stress constraints. The results of the proposed algorithm are compared with some other well-known meta-heuristic methods. The results show that the hybrid HTC algorithm improved the convergence rate and quickly obtained the optimal and desired design. The hybrid HTC algorithm can be adapted to solve other complex mathematical and optimization problems.

关键词： Meta-heuristic algorithms optimization of structures Charged system search algorithm teaching-learning-based optimization Hybrid charged system search based on TLBO algorithm

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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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Balancing stochastic two-sided assembly line with multiple constraints using hybrid teaching-learning-based optimization algorithm

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COMPUTERS & OPERATIONS RESEARCH 2017年第Jun.期82卷 102-113页

作者： Tang, Qiuhua Li, Zixiang Zhang, LiPing Zhang, Chaoyong Wuhan Univ Sci & Technol Dept Ind Engn Wuhan 430081 Hunan Peoples R China Huazhong Univ Sci Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hunan Peoples R China

Two-sided assembly lines are usually found in the factories which produce large-sized products. In most literatures, the task times are assumed to be deterministic while these tasks may have varying operation times in real application, causing the reduction of performance or even the infeasibility of the schedule. Moreover, the ignorance of some specific constraints including positional constraints, zoning constraints and synchronism constraints will result in the invalidation of the schedule. To solve this stochastic twosided assembly line balancing problem with multiple constraints, we propose a hybrid teaching-learhingbased optimization (HTLBO) approach which combines both a novel teaching-learning-based optimization algorithm for global search and a variable neighborhood search with seven neighborhood operators for local search. Especially, a new priority-based decoding approach is developed to ensure that the selected tasks satisfy most of the constraints identified by multiple thresholds of the priority value and to reduce the idle times related to sequence-dependence among tasks. Experimental results on benchmark problems demonstrate both remarkable efficiency and universality of the developed decoding approach, and the comparison among 11 algorithms shows the effectiveness of the proposed HTLBO. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Stochastic two-sided assembly line balancing teaching-learning-based optimization Variable neighborhood search Multiple constraints

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Optimum variable input speed for kinematic performance of Geneva mechanisms using teaching-learning-based optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2017年第10期231卷 1871-1883页

作者： Lin, W. Y. Tsai, Y. H. Hsiao, K. M. De Lin Inst Technol Dept Mech Engn 1 Lane 380Qingyan Rd New Taipei 23654 Taiwan Natl Chiao Tung Univ Dept Mech Engn Hsinchu Taiwan

An optimum design of variable input speed for the Geneva mechanism is aimed at improving the kinematic performance of the traditional Geneva mechanism by eliminating infinite angular jerks and reducing the peak angular acceleration of the Geneva wheel during the indexing motion. The normalized angular velocity and acceleration of the Geneva wheel corresponding to the normalized time are introduced. A polynomial function of the normalized time is used to describe the normalized angular position of the crank, and therefore, the corresponding polynomial coefficients are considered as the design variables. The optimum design task is very specialized and difficult to solve with some evolutionary and swarm optimization methods because of the extremely large range for the value of the design variable, arising from the utilization of a higher order polynomial for the normalized time parameter with a value between 0 and 1. A new evolutionary algorithm termed teaching-learning-based optimization comprises a teacher phase and a learner phase. In the teacher phase, the entire population can be gradually shifted to a more promising region, which may be very far from the relatively small initial region. The obtained optimal results are compared with those obtained using the length-adjustable deriving link method discussed in the literature. The findings show that the difference in the effectiveness of the variable input speed method and the length-adjustable driving link method for the reduction of the peak angular acceleration of the Geneva wheel is small.

关键词： Geneva mechanism kinematic performance optimization teaching-learning-based optimization variable input speed

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teaching-learning-based optimization (TLBO) in Hybridized with Fuzzy Inference System Estimating Heating Loads

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ENERGIES 2022年第21期15卷 8289页

作者： Foong, Loke Kok Binh Nguyen Le Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Duy Tan Univ Sch Engn & Technol Da Nang 550000 Vietnam

Nowadays, since large amounts of energy are consumed for a variety of applications, more and more emphasis is placed on the conservation of energy. Recent investigations have experienced the significant advantages of using metaheuristic algorithms. Given the importance of the thermal loads' analysis in energy-efficiency buildings, a new optimizer method, i.e., the teaching-learning based optimization (TLBO) approach, has been developed and compared with alternative techniques in the present paper to predict the heating loads (HLs). This model is applied to the adaptive neuro-fuzzy interface system (ANFIS) in order to overcome its computational deficiencies. A literature-based dataset acquired for residential buildings is used to feed these models. According to the results, all the applied models can appropriately predict and analyze the heating load pattern. based on the value of R-2 calculated for both testing and training (0.98933, 0.98931), teaching-learning-based optimization can help the adaptive neuro-fuzzy interface system to enhance the results' correlation. Also, the high R-2 value means that the model has high accuracy in the HL prediction. In addition, according to the estimated RMSE, the training error of TLBO-ANFIS in the testing and training stages was 0.07794 and 0.07984, respectively. The low value of root-mean-square error (RMSE) indicates that the TLBO-ANFIS method acts favorably in the estimation of the heating load for residential buildings.

关键词： adaptive neuro-fuzzy interface system residential buildings metaheuristic heating-load teaching-learning-based optimization

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Parameters Extraction of Photovoltaic Models Using Triple-Phase teaching-learning-based optimization

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IEEE ACCESS 2020年 8卷 69937-69952页

作者： Liao, Zuowen Chen, Zhikun Li, Shuijia Beibu Gulf Univ Coll Econ & Management Beibu Gulf Big Data Resources Utilisat Lab Qinzhou 535011 Peoples R China Beibu Gulf Univ Coll Resources & Environm Beibu Gulf Big Data Resources Utilisat Lab Guangxi Key Lab Marine Disaster Beibu Gulf Qinzhou 535011 Peoples R China China Univ Geosci Dept Comp Sci Wuhan 430074 Peoples R China

Parameters extraction of photovoltaic (PV) models is urgently desired for the simulation, control, and evaluation of PV systems. To accurately and reliably extract the parameters of different PV models, a triple-phase teaching-learning-based optimization (TPTLBO) is proposed in this paper. The novelty of TPTLBO lies in: i) teaching-learning-based optimization introduces a buffer phase and adopts a centroid strategy to update the position of intermediate learners, which further strengthens the exploration and exploitation;ii) the learners can select different phases and employ different learning strategies based on their knowledge level;iii) a dynamic control parameter replaces the original random parameter rand to enhance the search ability of algorithm. The parameters extraction performance of TPTLBO is verified through the single diode model, the double diode model, and three PV models. Experimental results demonstrate that TPTLBO achieves better performance in terms of accuracy and reliability compared to state-of-the-art algorithms.

关键词： Photovoltaic models parameter extraction teaching-learning-based optimization triple-phase

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