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TURKISH JOURNAL OF CIVIL ENGINEERING 2025年第2期36卷 81-95页

作者： Gunay, Arife Akbas, Sami Oguzhan Karadeniz Tech Univ Dept Civil Engn Trabzon Turkiye Gazi Univ Dept Civil Engn Ankara Turkiye

The goal of the this study is to investigate the applicability of the teaching-learning based optimization (TLBO) algorithm for modeling seepage in embankment dams. The input parameters selected for the models to be built are the values of permeability (ks), van Genuchten's suitability parameters alpha and n, whose effect on seepage has been investigated over the years due to their uncertainties. The validity of the TLBO was compared with that of conventional regression analysis (CRA) methods. Both methods were utilized with different regression forms. The parameters chosen as input are modeled as random variables with a log-normal distribution, and total discharge (Q) was obtained. Four statistical indices, that is, root mean square error, mean absolute error, average relative error and coefficient of determination, were used to evaluate the performance of the models. The equations obtained using TLBO algorithms can predict the total discharge in embankment dams better than CRA. In addition, the reliability of TLBO has been demonstrated by conducting analyses using the outputs of CRA as a benchmark.

关键词： Monte Carlo Simulation permeability van genuchten parameters seepage analysis teaching-learning based optimization

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Enhancing teaching learning based optimization algorithm through group discussion strategy for CEC 2017 benchmark problems

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Soft Computing 2025年第2期29卷 895-932页

作者： Sagheer, Muhammad Asif Jan, Muhammad Shah, Zahir Mashwani, Wali Khan Adeeb Khanum, Rashida Shutaywi, Meshal Institute of Numerical Sciences Kohat University of Science and Technology Khyber Pakhtunkhwa Kohat26000 Pakistan Department of Mathematical Sciences University of Lakki Marwat Khyber Pakhtunkhwa Lakki Marwat28420 Pakistan Jinnah College for Women University of Peshawar Khyber Pakhtunkhwa Peshawar25000 Pakistan Department of Mathematics College of Science and Arts King Abdul-Aziz University Rabigh Saudi Arabia

Meta-heuristics are utilized to handle challenging optimization problems, since conventional optimization techniques for such problems often fail or become stuck in the local optimum. teaching-learning based optimization algorithm (TLBO) is a prominent meta-heuristic that mimics the teaching-learning process. It is initially employed to tackle unconstrained optimization problems. The main obstacle to meta-heuristics is premature convergence. The strategy of forming groups of students is introduced in the teaching-learning process for mutual discussions and joint projects. Along with some advantages, like increased creativity, diversity of ideas, access to new information, and more critical thinking, the group discussion strategy also has few disadvantages, such as disagreements over ideas, group size sensitivity, dependency on others, and lengthened the completion time. If properly implemented, the strategy can be a useful tool in the teaching-learning process. To increase an algorithm local search capabilities and to produce solutions of diverse nature, it is essential to have sufficient knowledge about the optimum solution with enough population diversity. Through group discussion sufficient information with diverse views about the solution of the problem is obtained. Therefore, in this work, the group discussion strategy is embedded to the learner phase of TLBO to enhance it. In the strategy, the group of randomly selected individuals correspond to each candidate solution is made to minimize the happening of premature convergence. This strategy depends on the group size parameter;for which the sensitivity analysis is also performed to obtain the right/optimal value. The suggested algorithm is known as GTLBO, and the group size parameter sensitivity analysis generates its four versions, referred to as GTLBO2–GTLBO5, where the digits 2–5 represent the group size value. The performance of the proposed algorithms is evaluated by the unconstrained benchmark problems o

关键词： Group discussion strategy Meta-heuristic Sensitivity analysis teaching-learning based optimization Unconstrained optimization

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Four-bar linkage path generation through self-adaptive population size teaching-learning based optimization

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KNOWLEDGE-based SYSTEMS 2017年第Nov.1期135卷 180-191页

作者： Sleesongsom, S. Bureerat, S. Int Acad Aviat Ind King Mongkuts Inst Technol Ladkrabang Dept Aeronaut Engn & Commercial Pilot Bangkok 10520 Thailand Khon Kaen Univ Fac Engn Dept Mech Engn Sustainable & Infrastruct Res & Dev Ctr Khon Kaen 40002 Thailand

This paper proposes a new variant of teaching-learning based optimization (TLBO) for optimal path synthesis of a four-bar linkage. The new TLBO algorithm is called self-adaptive population size teaching learning based optimization (SAP-TLBO) which is altered from the original version by integrating a self adaptive strategy for population sizing. A new concept of path generation is designed for problems without prescribed timing, which is proposed to avoid the timing constraint. In the first stage, two traditional path generation test problems are used to test the proposed technique and the new optimizer. Then, the new optimizer is used to solve five mechanical design problems. The results show that the new design concept for path synthesis gives significantly better results than the classical exterior penalty function technique, whereas the computational time is increased. SAP-TLBO is significantly superior to its original version and other meta-heuristics. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Mechanism synthesis Path generation Self-adaptive strategy Constraint handling teaching-learning based optimization

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Modeling stream dissolved oxygen concentration using teaching-learning based optimization algorithm

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ENVIRONMENTAL EARTH SCIENCES 2015年第10期73卷 6565-6576页

作者： Bayram, Adem Uzlu, Ergun Kankal, Murat Dede, Tayfun Karadeniz Tech Univ Dept Civil Engn Fac Engn TR-61080 Trabzon Turkey

The aim of this study is to investigate the applicability of teaching-learning based optimization (TLBO) algorithm for the first time in modeling stream dissolved oxygen (DO) prediction. The input parameters selected from a surface water-quality study including 20 indicators for the models are water pH, temperature, electrical conductivity, and hardness, which were measured semimonthly at six monitoring sites selected in an untreated wastewater impacted urban stream during a year, due to their direct and indirect effect on DO concentration. The accuracy of TLBO method is compared with those of the artificial bee colony algorithm and conventional regression analysis methods. These methods are applied to four different regression forms: quadratic, exponential, linear, and power. There are 144 data for each water-quality indicator, 114 of which are designated for training and the rest for testing patterns in the models. To evaluate the performance of the models, five statistical indices, i.e., sum square error, root mean square error, mean absolute error, average relative error, and determination coefficient, are used. The TLBO method with quadratic form from among all models yielded better prediction, with an improvement of nearly 20 %. It can be concluded that the equations obtained by employing the TLBO algorithms predict the stream DO concentration successfully. Therefore, the employment of the TLBO algorithm by water resources and environment managers is encouraged and recommended for future studies.

关键词： Artificial bee colony algorithm Dissolved oxygen concentration Modeling Stream Harsit teaching-learning based optimization

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A novel teaching-learning based optimization approach for design of broad-band anti-reflection coatings

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SWARM AND EVOLUTIONARY COMPUTATION 2017年 34卷 68-74页

作者： Patel, Sanjaykumar J. Toshniwal, Aditi Kheraj, Vipul SV Natl Inst Technol Dept Appl Phys Surat 395007 Gujarat India

The design of thin-film multilayered anti-reflection (AR) coating is quite an intricate task due to highly nonlinear and complex dimensional search space, which includes many local minima. In this paper, a novel teaching-learning based optimization (TLBO) approach is employed to design ultra-low reflective coating over a broad wavelength-band using multilayer thin-film structures for optoelectronic devices. The algorithm is implemented using LabVIEW as a programming tool. Various design specific input parameters such as scanning range of wavelengths, step-size, angle of incidence, number of layers, the name and sequence of coating materials etc. are required to be fed by the user on the graphical user interface. The algorithm minimizes the average reflectivity computed over given wavelength range by tuning the thickness of layers in the multilayer stack. The reliability and evolution of design solution with iterations have been systematically investigated for different learner-sizes. Finally, using the optimized learner size and desired number of iterations, the optimum AR design is obtained in terms of the thickness of each layer for the multilayer AR coating. The effectiveness of the TLBO approach has been compared with that of an established algorithm, i.e. genetic algorithm (GA), by means of Wilcoxon singed ranked test. It is concluded that the TLBO can be a very efficient, simpler and relatively faster approach to address complex optimization problems such as broad-band AR coating designs.

关键词： Anti-reflection coating optimization algorithms teaching-learning based optimization Thin-films and 1abVIEW

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A teaching-learning based optimization approach for economic design of X-bar control chart

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APPLIED SOFT COMPUTING 2014年 24卷 643-653页

作者： Ganguly, Abhijeet Patel, Saroj Kumar Natl Inst Technol Dept Mech Engn Rourkela 769008 Odisha India

Being simple to use X-bar control chart has been most widely used in industry for monitoring and controlling manufacturing processes. Measurements of a quality characteristic in terms of samples are taken from the production process at regular interval and the sample means are plotted on this chart. Design of a control chart involves the selection of three parameters, namely the sample size (n), the sampling interval (h) and the width of control limits (k). In case of economic design, these three control chart parameters are selected in such a manner that the total cost of controlling the process is the least. The effectiveness of this design depends on the accuracy of determination of these three parameters. In this paper, a new efficient and effective optimization technique named as teaching-learning based optimization (TLBO) has been used for the global minimization of a loss cost function expressed as a function of three variables n, h and k in an economic model of X-bar chart based on unified approach. In this work, the TLBO algorithm has been modified to simplify the tuning of teaching factor. A MATLAB computer program has been developed for this purpose. A numerical example has been solved and the results are found to be better than the earlier published results. Further, the sensitivity analysis using fractional factorial design and analysis of variance have been carried out to identify the critical process and cost parameters affecting the economic design. (C) 2014 Elsevier B.V. All rights reserved.

关键词： Analysis of variance Economic design Fractional factorial design Loss cost teaching-learning based optimization X-bar chart

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Design of planar steel frames using teaching-learning based optimization

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ENGINEERING STRUCTURES 2012年 34卷 225-232页

作者： Togan, Vedat Karadeniz Tech Univ Dept Civil Engn TR-61080 Trabzon Turkey

This paper presents a design procedure employing a teaching-learning based optimization (TLBO) technique for discrete optimization of planar steel frames. TLBO is a nature-inspired search method that has been developed recently. It simulates the social interaction between the teacher and the learners in a class, which is summarized as teaching-learning process. The design algorithm aims to obtain minimum weight frames subjected to strength and displacement requirements imposed by the American Institute for Steel Construction (AISC) Load and Resistance Factor Design (LRFD). Designs are obtained selecting appropriate W-shaped sections from a standard set of steel sections specified by the AISC. Several frame examples from the literature are examined to verify the suitability of the design procedure and to demonstrate the effectiveness and robustness of the TLBO creating of an optimal design for frame structures. The results of the TLBO are compared to those of the genetic algorithm (GA), the ant colony optimization (ACO), the harmony search (HS) and the improved ant colony optimization (IACO) and they shows that TLBO is a powerful search and applicable optimization method for the problem of engineering design applications. (C) 2011 Elsevier Ltd. All rights reserved.

关键词： Structural design optimization frames teaching-learning based optimization

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Testing the performance of teaching-learning based optimization (TLBO) algorithm on combinatorial problems: Flow shop and job shop scheduling cases

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INFORMATION SCIENCES 2014年 276卷 204-218页

作者： Baykasoglu, Adil Hamzadayi, Alper Kose, Simge Yelkenci Dokuz Eylul Univ Fac Engn Dept Ind Engn Izmir Turkey Minist Sci Ind & Technol Directorate Gen Safety & Inspect Ind Prod Ankara Turkey

teaching-learning based optimization (TLBO) algorithm has been recently proposed in the literature as a novel population oriented meta-heuristic algorithm. It has been tested on some unconstrained and constrained non-linear programming problems, including some design optimization problems with considerable success. The main purpose of this paper is to analyze the performance of TLBO algorithm on combinatorial optimization problems first time in the literature. We also provided a detailed literature review about TLBO's applications. The performance of the TLBO algorithm is tested on some combinatorial optimization problems, namely flow shop (FSSP) and job shop scheduling problems (JSSP). It is a well-known fact that scheduling problems are amongst the most complicated combinatorial optimization problems. Therefore, performance of TLBO algorithm on these problems can give an idea about its possible performance for solving other combinatorial optimization problems. We also provided a comprehensive comparative study along with statistical analyses in order to present effectiveness of TLBO algorithm on solving scheduling problems. Experimental results show that the TLBO algorithm has a considerable potential when compared to the best-known heuristic algorithms for scheduling problems. (C) 2014 Elsevier Inc. All rights reserved.

关键词： teaching-learning based optimization Flow shop scheduling Job shop scheduling Makespan Meta-heuristic Combinatorial optimization

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An autonomous teaching-learning based optimization algorithm for single objective global optimization

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE SYSTEMS 2016年第3期9卷 506-524页

作者： Ge, Fangzhen Hong, Liurong Shi, Li Huaibei Normal Univ Sch Comp Sci & Technol Huaibei 235000 Peoples R China

teaching-learning based optimization is a newly developed intelligent optimization algorithm. It imitates the process of teaching and learning simply and has better global searching capability. However, some studies have shown that TLBO is good at exploration but poor at exploitation and often falls into local optimum for certain complex problems. To address these issues, a novel autonomous teaching-learning based optimization algorithm is proposed to solve the global optimization problems on the continuous space. Our proposed algorithm is remodeled according to the three phases of the teaching and learning process, learning from a teacher, mutual learning and self-learning among students instead of two phases of the original one. Moreover, the motivation and autonomy of students are considered in our proposed algorithm, and the expressions of autonomy are formulated. The performance of our proposed algorithm is compared with that of the related algorithms through our experimental results. The results indicate the proposed algorithm performs better in terms of the convergence and optimization capability.

关键词： teaching-learning based optimization Global optimization Autonomy learning Desires

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Failure correction of linear antenna array using complex method enhanced teaching-learning based optimization algorithm

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JOURNAL OF ELECTROMAGNETIC WAVES AND APPLICATIONS 2019年第17期33卷 2354-2371页

作者： Luo, Zailei Han, Bin He, Xueming Zhao, Dexin Natl Innovat Inst Def Technol Adv Interdisciplinary Technol Res Ctr Beijing Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan Hubei Peoples R China China Maritime Police Acad Ningbo Zhejiang Peoples R China

The relatively high probability of elements failure is a concerning problem for large scale phased antenna arrays, which leads to degrade the antenna?s performance significantly. The array performance degradation can be corrected by re-optimizing the array excitations. teaching-learning based optimization (TLBO) is newly proposed swarm intelligence based evolutionary algorithm, which imitates the social behavior of teaching-learning process in the classroom. In this paper, an effective approach using complex method enhanced teaching-learning based optimization (ETLBO) algorithm is proposed for correction of antenna arrays in the presence of faulty elements. Numerical simulation results show that the proposed ETLBO algorithm having more advantages in finding global optimal solution than original TLBO algorithms. Experimental results of a 32-element phased array are presented to demonstrate that the proposed ETLBO algorithm is effective in practical engineering field.

关键词： Array failure correction evolutionary algorithms teaching-learning based optimization linear antenna array

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