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Parameters identification of photovoltaic models using self-adaptive teaching-learning-based optimization

ENERGY CONVERSION AND MANAGEMENT 2017年第Aug.期145卷 233-246页

作者： Yu, Kunjie Chen, Xu Wang, Xin Wang, Zhenlei East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Jiangsu Univ Sch Elect & Informat Engn Zhenjiang 212013 Jiangsu Peoples R China Shanghai Jiao Tong Univ Ctr Elect & Elect Technol Shanghai 200240 Peoples R China

Parameters identification of photovoltaic (PV) model based on measured current-voltage characteristic curves plays an important role in the simulation and evaluation of PV systems. To accurately and reliably identify the PV model parameters, a self-adaptive teaching-learning-based optimization (SATLBO) is proposed in this paper. In SATLBO, the learners can self-adaptively select different learning phases based on their knowledge level. The better learners are more likely to choose the learner phase for improving the population diversity, while the worse learners tend to choose the teacher phase to enhance the convergence rate. Thus, learners at different levels focus on different searching abilities to efficiently enhance the performance of algorithm. In addition, to improve the searching ability of different learning phases, an elite learning strategy and a diversity learning method are introduced into the teacher phase and learner phase, respectively. The performance of SATLBO is firstly evaluated on 34 benchmark functions, and experimental results show that SATLBO achieves the first in ranking on the overall performance among nine algorithms. Then, SATLBO is employed to identify parameters of different PV models, i.e., single diode, double diode, and PV module. Experimental results indicate that SATLBO exhibits high accuracy and reliability compared with other parameter extraction methods. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Photovoltaic model Parameter identification teaching-learning-based optimization learning strategy

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A novel approach for dynamic flow simulation of gas pipelines using teaching-learning-based optimization algorithm

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2019年第9期233卷 3085-3099页

作者： Pourfard, Ali Khanmirza, Esmaeel Madoliat, Reza Iran Univ Sci & Technol Dept Mech Engn POB 16765-163 Tehran Iran

Simulation of a natural gas network operation is a prerequisite for optimization and control tasks. Treating gas in a transient manner is necessary for accurate simulation of gas networks. However, solving the governing nonlinear partial differential equations of pipe flows is a challenging task. In this paper, a novel approach is proposed based on using an intelligent algorithm called teaching-learning-based optimization. This approach simplifies transient simulation of gas networks with a specified type of boundary conditions. teaching-learning-based optimization estimates different values for network inlet flow rates. Then by knowing the inlet boundary conditions of the network, the discretized flow equations become linear and the flow equations of each pipe can be solved independently. Thus, the network outlet flow variables can be easily obtained. The differences of obtained and actual network outlet flow rates are considered as a cost function or error. Finally, this intelligent algorithm determines the optimum inlet flow rates at each time level, which minimize the error. The proposed approach is implemented on the in-service gas network. To validate the simulation results, a conventional gradient-based method called trust region dogleg is also used for simulation of the gas network. The comparison of numerical results confirms the accuracy and efficiency of this approach, while it is more computationally efficient. Moreover, the substitution of teaching-learning-based optimization with another powerful intelligent optimization algorithm would not improve the performance of the proposed approach.

关键词： Transient flow gas pipelines mathematical model intelligent algorithms teaching-learning-based optimization

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An effective hybrid teaching-learning-based optimization algorithm for permutation flow shop scheduling problem

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ADVANCES IN ENGINEERING SOFTWARE 2014年第0期77卷 35-47页

作者： Xie, Zhanpeng Zhang, Chaoyong Shao, Xiniyu Lin, Wenwen Zhu, Haiping Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hunan Peoples R China

Permutation flow shop scheduling (PFSP) is among the most studied scheduling settings. In this paper, a hybrid teaching-learning-based optimization algorithm (HTLBO), which combines a novel teaching-learning-based optimization algorithm for solution evolution and a variable neighborhood search (VNS) for fast solution improvement, is proposed for PFSP to determine the job sequence with minimization of makespan criterion and minimization of maximum lateness criterion, respectively. To convert the individual to the job permutation, a largest order value (LOV) rule is utilized. Furthermore, a simulated annealing (SA) is adopted as the local search method of VNS after the shaking procedure. Experimental comparisons over public PFSP test instances with other competitive algorithms show the effectiveness of the proposed algorithm. For the DMU problems, 19 new upper bounds are obtained for the instances with makespan criterion and 88 new upper bounds are obtained for the instances with maximum lateness criterion. (C) 2014 Elsevier Ltd. All rights reserved.

关键词： Permutation flow shop scheduling problem teaching-learning-based optimization algorithm Variable neighborhood search Simulated annealing Makespan Maximum lateness

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Diagnosis of Multiple Wiring Faults Using Time-Domain Reflectometry and teaching-learning-based optimization

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ELECTROMAGNETICS 2015年第1期35卷 10-24页

作者： Boudjefdjouf, H. Mehasni, R. Orlandi, A. Bouchekara, H. R. E. H. De Paulis, F. Smail, M. K. Univ Constantine 1 Dept Elect Engn Elect Engn Lab Constantine Constantine 25000 Algeria Univ Aquila Dept Ind & Informat Engn & Econ Electromagnet Compatibil Lab I-67100 Laquila Italy Univ Paris Est IFSTTAR F-77747 Champs Sur Marne France

Time-domain reflectometry has proven to be one of the best methods for wiring network diagnosis, and it can easily be applied to the detection and localization of defects, while only requiring one access point to the wiring network. In this article, a novel approach combining the time-domain reflectometry response extracted from vector network analyzer measurements and the teaching-learning-based optimization technique is developed and applied to the diagnosis of wiring networks. The proposed approach consists of two steps. In the first step, propagation along the cables is modeled using the forward model. In the second step, teaching-learning-based optimization is used to solve the inverse problem to deduce physical information about the defects in the wiring network. The proposed approach has been successfully tested on several cases and for different configurations. Comparisons of the proposed time-domain reflectometry/teaching-learning-based optimization approach results with measurements reveal that this approach has a high potential and is very effective for wiring network diagnosis.

关键词： finite-difference time-domain method multiple wiring diagnosis time-domain reflectometry teaching-learning-based optimization

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Adaptive teaching-learning-based optimization with experience learning to identify photovoltaic cell parameters

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ENERGY REPORTS 2021年 7卷 4114-4125页

作者： Mi, Xianyan Liao, Zuowen Li, Shuijia Gu, Qiong Beibu Gulf Univ Coll Econ & Management Qinzhou 535000 Peoples R China Beibu Gulf Univ Beibu Gulf Ocean Dev Res Ctr Qinzhou 535000 Peoples R China China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Hubei Univ Arts & Sci Sch Comp Engn Xiangyang 441053 Peoples R China

Parameter identification of photovoltaic cell and module plays a key role in the simulation, evaluation, control, and optimization of photovoltaic systems. In order to improve the accuracy and reliability of the identified parameters, in this paper, a new adaptive teaching-learning-based optimization with experience learning referred as ELATLBO, is proposed. In ELATLBO, the population is first sorted according to the objective function value, and then it is divided into two parts: fit solutions with good objective function values and inferior solutions with poor objective function values. The fit solution that selects the teacher phase with experience learning is used for local search to improve the exploitation capability of the algorithm. While the inferior solution that selects the learner phase with experience learning is applied for global search to enhance the exploration capability of the algorithm. The performance of ELATLBO is verified by testing the photovoltaic cells and module parameter identification problems, i.e., the single diode model, the double diode model, and the single diode photovoltaic module. The simulated result shows that the proposed ELATLBO exhibits remarkable performance on the accuracy and reliability when compared with other reported parameter identification techniques, especially for the double diode model. (C) 2021 The Authors. Published by Elsevier Ltd.

关键词： Parameter identification Photovoltaic cell teaching-learning-based optimization Experience learning

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Landslide susceptibility modeling based on ANFIS with teaching-learning-based optimization and Satin bowerbird optimizer

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Geoscience Frontiers 2021年第1期12卷 93-107页

作者： Wei Chen Xi Chen Jianbing Peng Mahdi Panahi Saro Lee College of Geology and Environment Xi’an University of Science and TechnologyXi’an 710054China Key Laboratory of Coal Resources Exploration and Comprehensive Utilization Ministry of Natural ResourcesXi’an 710021China Department of Geological Engineering Chang’an UniversityXi’an 710054China Division of Science Education College of Education#4-301Gangwondaehak-gil Chuncheon-siKangwon National UniversityGangwon-do 24341South Korea Geoscience Platform Research Division Korea Institute of Geoscience and Mineral Resources(KIGAM)124Gwahak-ro Yuseong-guDaejeon 34132South Korea Department of Geophysical Exploration Korea University of Science and Technology217 Gajeong-ro Yuseong-guDaejeon 34113South Korea

As threats of landslide hazards have become gradually more severe in recent decades,studies on landslide prevention and mitigation have attracted widespread attention in relevant domains.A hot research topic has been the ability to predict landslide susceptibility,which can be used to design schemes of land exploitation and urban development in mountainous *** this study,the teaching-learning-based optimization(TLBO)and satin bowerbird optimizer(SBO)algorithms were applied to optimize the adaptive neuro-fuzzy inference system(ANFIS)model for landslide susceptibility *** the study area,152 landslides were identified and randomly divided into two groups as training(70%)and validation(30%)***,a total of fifteen landslide influencing factors were *** relative importance and weights of various influencing factors were determined using the step-wise weight assessment ratio analysis(SWARA)***,the comprehensive performance of the two models was validated and compared using various indexes,such as the root mean square error(RMSE),processing time,convergence,and area under receiver operating characteristic curves(AUROC).The results demonstrated that the AUROC values of the ANFIS,ANFIS-TLBO and ANFIS-SBO models with the training data were 0.808,0.785 and 0.755,*** terms of the validation dataset,the ANFISSBO model exhibited a higher AUROC value of 0.781,while the AUROC value of the ANFIS-TLBO and ANFIS models were 0.749 and 0.681,***,the ANFIS-SBO model showed lower RMSE values for the validation dataset,indicating that the SBO algorithm had a better optimization ***,the processing time and convergence of the ANFIS-SBO model were far superior to those of the ANFIS-TLBO ***,both the ensemble models proposed in this paper can generate adequate results,and the ANFIS-SBO model is recommended as the more suitable model for landslide susceptibility assessment in the study area

关键词： Landslide susceptibility Step-wise weight assessment ratio analysis Adaptive neuro-fuzzy fuzzy inference system teaching-learning-based optimization Satin bowerbird optimizer

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Optimal power flow using teaching-learning-based optimization technique

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ELECTRIC POWER SYSTEMS RESEARCH 2014年 114卷 49-59页

作者： Bouchekara, H. R. E. H. Abido, M. A. Boucherma, M. Univ Constantine 1 Dept Elect Engn LEC Constantine Elect Engn Lab Constantine 25000 Algeria King Fahd Univ Petr & Minerals Dept Elect Engn Dhahran 31261 Saudi Arabia

teaching-learning-based optimization is a rising star among metaheuristic techniques with highly competitive performances. This technique is based on the influence of a teacher on learners. In this paper, the teaching-learning-based optimization technique is used to solve the optimal power flow problem. In order to show the effectiveness of the proposed method, it has been applied to the standard IEEE 30-bus and IEEE 118-bus test systems for different objectives that reflect the performances of the power system. Furthermore, the obtained results using the proposed technique have been compared to those obtained using other techniques reported in the literature. The obtained results and the comparison with other techniques indicate that the teaching-learning-based optimization technique provides effective and robust high-quality solution when solving the optimal power flow problem with different complexities. (C) 2014 Elsevier B.V. All rights reserved.

关键词： teaching-learning-based optimization Metaheuristic Optimal power flow Power system optimization Voltage profile Voltage stability

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A survey of teaching-learning-based optimization

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NEUROCOMPUTING 2019年 335卷 366-383页

作者： Zou, Feng Chen, Debao Xu, Qingzheng Huaibei Normal Univ Sch Phys & Elect Informat Huaibei 235000 Peoples R China Natl Univ Def Technol Coll Informat & Commun Xian 710106 Shaanxi Peoples R China

Over past few decades, swarm intelligent algorithms based on the intelligent behaviors of social creatures have been extensively studied and applied for all kinds of optimization areas. teaching-learning-based optimization (TLBO) algorithm which imitates the teaching-learning process in a classroom, is one of population-based heuristic stochastic swarm intelligent algorithms. TLBO executes through similar iterative evolution processes as utilized by a standard evolutionary algorithm. Unlike traditional evolutionary algorithms and swarm intelligent algorithms, the iterative computation process of TLBO is divided into two phases and each phase executes iterative learning operation. Since its introduction by Rao and his team in 2010, TLBO has attracted more and more researchers' attention because of some of its strengths such as simple concept, without algorithm-specific parameters, rapid convergence and easy implementation yet effectiveness. In this paper we attempt to provide a brief review of the basic concepts of TLBO and a comprehensive survey of its prominent variants and its typical application, and the theoretical analysis conducted on TLBO so far. We hope that this survey can be very beneficial for the researchers engaged in the study of TLBO. (C) 2018 Elsevier B.V. All rights reserved.

关键词： teaching-learning-based optimization Survey Modification Hybridization Applications

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Multi-objective optimization of community detection using discrete teaching-learning-based optimization with decomposition

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INFORMATION SCIENCES 2016年第0期369卷 402-418页

作者： Chen, Debao Zou, Feng Lu, Renquan Yu, Lei Li, Zheng Wang, Jiangtao HuaiBei Normal Univ Sch Phys & Elect Informat Huaibei Anhui Peoples R China Hangzhou Dianzi Univ Inst Informat & Automat Hangzhou Zhejiang Peoples R China HuaiBei Normal Univ Sch Comp Sci & Technol Huaibei Peoples R China

Community detection has been an active field of study in complex network analysis in recent years. It can be modeled as a seriously nonlinear optimization problem. Many intelligent optimization techniques have shown promising results for this problem. The teaching learning-based optimization (TLBO) algorithm is a recently proposed swarm intelligent algorithm. In this paper, a discrete variant of TLBO (DTLBO) is proposed to address discrete optimization problems. In the proposed method, the learner representation scheme is redefined, and the updating rules for learners are also redesigned. Moreover, based on the proposed discrete variant DTLBO, a multi-objective discrete method (MODTLBO/D) is proposed to solve community detection problems for complex networks. The multi-objective decomposition mechanism is adopted and neighbor-based mutation is introduced to maintain the diversity of the population and avoid being trapped in the local optima. Finally, to verify the performance of the proposed algorithm, real-world networks are examined. The experimental results indicate that MODTLBO/D is effective compared with other algorithms used for community detection in complex networks. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Community detection Discrete optimization teaching-learning-based optimization Multi-objective optimization Decomposition

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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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