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Experimental and numerical investigation of bridge pier scour estimation using ANFIS and teaching-learning-based optimization methods

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ENGINEERING WITH COMPUTERS 2019年第3期35卷 1103-1120页

作者： Hassanzadeh, Yousef Jafari-Bavil-Olyaei, Amin Aalami, Mohammad-Taghi Kardan, Nazila Univ Tabriz Dept Civil Engn Tabriz Iran Azarbaijan Shahid Madani Univ Dept Civil Engn Tabriz Iran

Studies have shown that the major cause of the bridge failures is the local scour around the pier foundations or their abutments. The local scour around the bridge pier is occurred by changing the flow pattern and creating secondary vortices in the front and rear of the bridge piers. Until now, many researchers have proposed empirical equations to estimate the bridge pier scour based on laboratory and field datasets. However, scale impact, laboratory simplification, natural complexity of rivers and the personal judgement are among the main causes of inaccuracy in the empirical equations. Therefore, due to the deficiencies and disadvantages of existing equations and the complex nature of the local scour phenomenon, in this study, the adaptive network-based fuzzy inference system (ANFIS) and teaching-learning-based optimization (TLBO) method were combined and used. The parameters of the ANFIS were optimized by using TLBO optimization method. To develop the model and validate its performance, two datasets were used including laboratory dataset that consisted of experimental results from the current study and previous ones and the field dataset. In total, 27 scaled experiments of different types of pier groups with different cross sections and side slopes were carried out. To evaluate the model ability in prediction of scour depth, results were compared to the standard ANFIS and empirical equations using evaluation functions including Hec-18, Froehlich and Laursen and Toch equations. The results showed that adding TLBO to the standard ANFIS was efficient and can increase the model capability and reliability. Proposed model achieved better results than Laursen and Toch equation which had the best results among empirical relationships. For instance, proposed model in comparison with the Laursen and Toch equation, based on the RMSE function, yielded 50.4% and 71.8% better results in laboratory and field datasets, respectively.

关键词： Adaptive neuro-fuzzy inference system teaching-learning-based optimization Empirical equations Scour depth Bridge pier

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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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A novel hybrid teaching-learning-based optimization algorithm for the classification of data by using extreme learning machines

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2019年第2期27卷 1523-1533页

作者： Sevinc, Ender Dokeroglu, Tansel Turkish Aeronaut Assoc Univ Fac Engn Dept Comp Engn Ankara Turkey TED Univ Fac Engn Dept Comp Engn Ankara Turkey

Data classification is the process of organizing data by relevant categories. In this way, the data can be understood and used more efficiently by scientists. Numerous studies have been proposed in the literature for the problem of data classification. However, with recently introduced metaheuristics, it has continued to be riveting to revisit this classical problem and investigate the efficiency of new techniques. teaching-learning-based optimization (TLBO) is a recent metaheuristic that has been reported to be very effective for combinatorial optimization problems. In this study, we propose a novel hybrid TLBO algorithm with extreme learning machines (ELM) for the solution of data classification problems. The proposed algorithm (TLBO-ELM) is tested on a set of UCI benchmark datasets. The performance of TLBO-ELM is observed to be competitive for both binary and multiclass data classification problems compared with state-of-the-art algorithms.

关键词： teaching-learning-based optimization extreme learning machines metaheuristic classification feature selection

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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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A modified teaching-learning-based optimization algorithm for numerical function optimization

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INTERNATIONAL JOURNAL OF MACHINE learning AND CYBERNETICS 2019年第6期10卷 1357-1371页

作者： Niu, Peifeng Ma, Yunpeng Yan, Shanshan Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Hydropower Stn Adm Taolinkou Reservoir Qinhuangdao 066004 Hebei Peoples R China

In this paper, a kind of modified teaching-learning-based optimization algorithm (MTLBO) is proposed to enhance the solution quality and accelerate the convergence speed of the conventional TLBO. Compared with TLBO, the MTLBO algorithm possesses different updating mechanisms of the individual solution. In teacher phase of the MTLBO, the students are divided into two groups according to the mean result of learners in all subjects. Moreover, the two groups present different updating strategies of the solution. In learner phase, the students are still divided into two groups, where the first group includes the top half of the students and the second group contains the remaining students. The first group members increase their knowledge through interaction among themselves and study independently. The second group members increase their marks relying on their teacher. According to the above-mentioned updating mechanisms, the MTLBO can provide a good balance between the exploratory and exploitative capabilities. Performance of the proposed MTLBO algorithm is evaluated by 23 unconstrained numerical functions and 28 CEC2017 benchmark functions. Compared with TLBO and other several state-of-the-art optimization algorithms, the results indicate that the MTLBO shows better solution quality and faster convergence speed.

关键词： teaching-learning-based optimization Modified teaching-learning-based optimization Exploratory and exploitative capabilities Unconstrained numerical functions CEC2017

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Parameter extraction of photovoltaic models using an improved teaching-learning-based optimization

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ENERGY CONVERSION AND MANAGEMENT 2019年第Apr.期186卷 293-305页

作者： Li, Shuijia Gong, Wenyin Yan, Xuesong Hu, Chengyu Bai, Danyu Wang, Ling Gao, Liang China Univ Geosci Sch Comp Sci Wuhan 430074 Hubei Peoples R China Nanjing Forestry Univ Coll Econ & Management Nanjing 210037 Jiangsu Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Hubei Peoples R China

Accurate and reliable parameter extraction of photovoltaic (PV) models is urgently desired for the simulation, evaluation, control, and optimization of PV systems. Although many meta-heuristic algorithms have been used to extract the PV parameters, the extracted parameters are usually not very accurate and reliable. To accurately and reliably extract the parameters of different PV models, an improved teaching-learning-based optimization (ITLBO) algorithm is proposed in this paper. The novelty of ITLBO lies primarily in the improved teaching and learning strategies with two improvements: (i) the teacher adopts different teaching strategies according to learner levels in the teacher phase;and (ii) in the learner phase, a new learning strategy is proposed to balance exploration and exploitation. The performance of ITLBO is verified by extracting the parameters of the single diode model, the double diode model, and three PV modules. The experimental results indicate that ITLBO obtains better performance with respect to accuracy and reliability compared to the other algorithms.

关键词： Photovoltaic models Parameter extraction teaching-learning-based optimization

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Confined teaching-learning-based optimization with variable search strategies for continuous optimization

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INFORMATION SCIENCES 2019年 500卷 34-47页

作者： Tsai, Hsing-Chih Natl Taiwan Univ Sci & Technol Dept Civil & Construct Engn 43Sect 4Keelung Rd Taipei Taiwan

The well-known optimization approach teaching-learning-based optimization (TLBO) is modified by using a confined TLBO (CTLBO) to eliminate the teaching factor. Different settings are suggested for various types of search factors, as they are used for different purposes. In addition, crossover frequencies are introduced into TLBO to prevent premature convergence. Furthermore, eight new mutation strategies are introduced to the teacher phase, and four new mutation strategies to the student phase to enhance the algorithm's exploitation and exploration capabilities. The experimental results show that the proposed versions, especially those that either adopted low crossover frequencies or implemented various mutation strategies, performed particularly well in achieving fast convergence speeds in the early stages, reaching convergence precision at lower cost, arriving at convergence plateaus at either lower cost or higher precision, handling tests of composition functions well, and achieving competitive performance on CEC2015 test problems. (C) 2019 Elsevier Inc. All rights reserved.

关键词： teaching-learning-based optimization Continuous optimization teaching factor Crossover rates Mutation strategies Search factors

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Hybrid teaching-learning-based optimization and neural network algorithm for engineering design optimization problems

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KNOWLEDGE-based SYSTEMS 2020年 187卷 104836-000页

作者： Zhang, Yiying Jin, Zhigang Chen, Ye Tianjin Univ Sch Elect & Informat Engn Tianjin 300072 Peoples R China Hainan Univ Coll Appl Sci & Technol Haikou 570228 Hainan Peoples R China

Neural network algorithm (NNA) is one of the newest meta-heuristic algorithms, which is inspired by biological nervous systems and artificial neural networks. Benefiting from the unique structure of artificial neural networks, NNA has good global search ability. However, slow convergence is its drawback that restricts its practical application. teaching-learning-based optimization (TLBO) is an algorithm without any effort for fine tuning initial parameters, which has fast convergence speed while it is easy to fall into local optimum in solving complex global optimization problems. Considering the features of NNA and TLBO, an effective hybrid method based on TLBO and NNA, named TLNNA, is proposed for solving engineering optimization problems. The performance of TLNNA for 30 well-known unconstrained benchmark functions and 4 challenging engineering optimization problems is examined and the optimization results are compared with other competitive meta-heuristic algorithms. Such comparisons suggest that TLNNA has not only good global search ability of NNA but also fast convergence speed of TLBO and is more successful for most test problems in terms of solution quality and computational efficiency. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Neural network algorithm Artificial neural networks teaching-learning-based optimization Engineering optimization

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A hybrid adaptive teaching-learning-based optimization and differential evolution for parameter identification of photovoltaic models

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ENERGY CONVERSION AND MANAGEMENT 2020年 225卷 113474-113474页

作者： Li, Shuijia Gong, Wenyin Wang, Ling Yan, Xuesong Hu, Chengyu China Univ Geosci Sch Comp Sci Wuhan 430074 Peoples R China Tsinghua Univ Dept Automat Beijing 100084 Peoples R China

Photovoltaic (PV) systems play an important role in today's power systems because they can convert solar energy directly into electricity. However, theirs conversion performance depends mainly on the PV models unknown parameters. Due to the complex characteristics of the equivalent circuit equation of the PV model, parameter identification of PV models remains a very popular and challenging task in PV system optimization. In this paper, a hybrid adaptive teaching-learning-based optimization (TLBO) with differential evolution (DE), referred to as ATLDE, is proposed to accurately and reliably identify the unknown parameters of PV models. In ATLDE, three improvements are introduced: i) the learners' ranking probability is presented to adaptively choose the teacher or learner phase of TLBO;ii) based on the learners' ranking probability, an enhanced teaching manner with an adaptive teaching factor TF is proposed to make use of the exploitation abilities of better learners in the teacher phase;iii) DE is embedded in the learner phase to improve population diversity and encourage wider exploration of the search space. In order to verify the performance of ATLDE, it is applied to parameter identification of different PV models, including the single diode model, the double diode model, and two PV panel module models. The experimental results demonstrate that our approach has great competitiveness in terms of accuracy and reliability. Therefore, the proposed algorithm can be an effective and efficient alternative for PV model parameter identification problems.

关键词： Parameter identification Photovoltaic models teaching-learning-based optimization Differential evolution

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A teaching-learning-based optimization Algorithm for the Resource-Constrained Project Scheduling Problem 4th

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4th International Conference on Harmony Search, Soft Computing and Applications (ICHSA)

作者： Joshi, Dheeraj Mittal, M. L. Kumar, Manish Swami Keshvanand Inst Technol Management & Gramot Dept Mech Engn Jaipur Rajasthan India Malaviya Natl Inst Technol Dept Mech Engn Jaipur 302017 Rajasthan India

ISBN: (纸本)9789811307614;9789811307607

In this paper, a recently introduced population-based metaheuristic known as teaching-learning-based optimization algorithm (TLBO) is used to find solution for the resource-constrained project scheduling problem (RCPSP). The RCPSP is considered in its basic form, wherein activities are non-preemptive in nature and their execution is possible in a single mode only. The scheduling objective chosen is minimizing the makespan or total project duration. TLBO algorithm in its original form employs two phases, namely the teacher phase and the learner phase to reach a global optimum solution. In order to increase the exploitation and exploration capabilities of the basic TLBO the concepts of elitism and mutation as used in genetic algorithm (GA) have been introduced. An activity list representation is used to represent a learner (solution) and to derive the schedule from this activity list a serial schedule generation scheme is used as a decoding procedure. It has been found after the computational experiment on a test problem from the literature that proposed TLBO gives results competitive to the other metaheuristics like GA and particle swarm optimization (PSO). In addition, it offers the inherent advantage of less parameter to tune and can, therefore, be used as an effective method to solve RCPSP and its other variants as well.

关键词： Resource-constrained project scheduling Metaheuristic teaching-learning-based optimization

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