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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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Multi-criteria evolutionary optimization of a traffic light using genetics algorithm and teaching-learning based optimization

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EXPERT SYSTEMS 2024年第2期41卷 e13487-e13487页

作者： Yektamoghadam, Hossein Nikoofard, Amirhossein Behzadi, Masoumeh Khosravy, Mahdi Dey, Nilanjan Witkowski, Olaf Univ Tehran Coll Engn Sch Elect & Comp Engn Tehran Iran KN Toosi Univ Technol Dept Elect Engn Tehran Iran Cross Compass Ltd Cross Labs Tokyo Japan Techno Int New Town Dept Comp Sci & Engn Kolkata India

Today, the development of urbanization and increasing the number of vehicles has resulted in displeased consequences like traffic congestion and vehicle queuing. The vast majority of countries in the world encounter the challenge of the explosive rise in traffic demand. In this regard, it is necessary to meet traffic demand in transport networks, especially in metropolitans. In traffic management and shortening the trip duration, traffic lights on the signalized intersections play an essential role in urban pathways. This work provides a multi-criteria decision-making method for optimum traffic light control in an isolated corner. The main idea involves establishing a set of sub-optimal solutions for traffic light timing and selecting the best one among the diverse solutions. We have mathematically modelled the problem as an optimization problem to achieve an optimal solution with less waiting time for vehicles in intersections and the lowest cost. Genetic algorithm (GA) and teaching-learning-based optimization (TLBO) are utilized for each phase to create a set of suitable timing scenarios. The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method is used to identify the best scenario, considering both waiting vehicles and traffic capacity as decision criteria. Its efficiency has been demonstrated over three different traffic volumes. Also, in a real-world implementation, its practical capability has been approved at a crossroads in Mashhad, Iran. The simulations indicate the improvement in the number of vehicles waiting behind the crossroad and the traffic capacity by 10% and 6.76% compared to the existing signal timing of the studied intersection, respectively.

关键词： evolutionary algorithm evolutionary optimization genetic algorithm teaching-learning based optimization traffic light control

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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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Boosting constrained teaching-learning based optimization algorithm through group discussion

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SCIENTIFIC AFRICAN 2024年 24卷

作者： Sagheer, Muhammad Jan, Muhammad Asif Bonyah, Ebenezer Mashwani, Wali Khan Shah, Zahir Khanum, Rashida Adeeb Kohat Univ Sci & Technol Inst Numer Sci Kohat 26000 Khyber PakhtunK Pakistan Akenten Appiah Menka Univ Skills Training & Entrep Dept Math Educ Kumasi Ghana Univ Airlangga Fac Sci & Technol Dept Math Surabaya 60115 Indonesia Univ Lakki Marwat Dept Math Sci Lakki Marwat 28420 Khyber Pakhtunk Pakistan Univ Peshawar Jinnah Coll Women Peshawar 25000 Khyber Pakhtunk Pakistan

teaching-learning based optimization algorithm (TLBO) is an efficient population-based natureinspired meta-heuristic designed initially to solve unconstrained optimization problems. TLBO being a swam intelligence based algorithm is renowned for fast convergence. This characteristic, however, occasionally causes it to converge too soon to a non-globally optimal solution. Group discussion techniques are used in educational institutions all over the world to help participants to exchange ideas and information and to improve their skills. This work imitates the outlined strategy, therefore innovating TLBO. Here, a group of virtual students is selected corresponding to each student in the learner phase of TLBO to produce diversity in the population and to balance the exploration and exploitation capabilities of it, hence reducing the likelihood of early convergence. GTLBO stands for the new algorithm that incorporates group discussion. It is essential to incorporate some constraint handling techniques (CHTs) into the TLBO framework in order to handle constrained optimization problems (COPs). The most recent, effective, and enhanced version of the widely used CHT stochastic ranking (SR) is called hybrid stochastic ranking (HSR). HSR is added to the GTLBO structure as a selection operator for managing COPs;the resulting setup is referred to as HSR-GTLBO. Additionally, the suggested algorithm's group size for discussion is sensitive, resulting in a number of constrained variants. The efficacy of these variations is assessed with the suit CEC 2017's constrained problems. In conformity with the parameters settlement, comparison, and ranking criteria of CEC 2017, the simulation results of the proposed versions are compared and ranked using the most advanced state-of-the-art algorithms. As a result, in the competition, the newly developed HSR-GTLBO variants placed first, second, and third.

关键词： Meta-heuristics Nature-inspired algorithms teaching-learning based optimization Hybrid stochastic ranking

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Hybridizing teaching-learning based optimization with GA and PSO: Case Study of Supply Chain Network Model

Hybridizing Teaching-Learning Based Optimization with GA and...

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International Conference on Computational Science and Computational Intelligence (CSCI)

作者： Gen, Mitsuo Anudari, Chuluunsukh Yun, YoungSu Fuzzy Log Syst Inst Tokyo Japan Tokyo Univ Sci Tokyo Japan Chosun Univ Int Cooperat Team Gwangju South Korea Chosun Univ Sch Business Adm Gwangju South Korea

ISBN: (纸本)9781665458412

Design and optimization of logistics and supply chain management (SCM) network is a very important issue, which plans, implements and controls the efficient, effective forward and reverses flows and storage of goods, services and related information between the point of origin and the point of consumption to meet customers' requirements. The structure of each stage of the supply chain is completed by a set of arcs connected to the nodes and representing the flow of goods: each arc has a weight proportional to the cost incurred in between the two nodes it connects together. In this paper, we first formulate the nonlinear 0-1 integer programming model of the SC network consisting of suppliers, manufacturers, DCs, and customers. Secondary we propose a hybridized teaching-learning based optimization (TLBO) with genetic algorithm (GA) and particle swarm optimization (PSO) combined Cauchy distribution to solve the SC network models. Lastly, we demonstrate the proposed GA+PSO+TLBO method outperformed GA, TLBO, GA+TLBO under the same computational environment for two numerical experiments of the several scaled SC network models.

关键词： Supply chain network hybrid metaheuristic approach genetic algorithm particle swarm optimization teaching-learning based optimization

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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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Minimization of total completion time on a batch processing machine with arbitrary release dates: an effectual teaching-learning based optimization approach

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PRODUCTION ENGINEERING-RESEARCH AND DEVELOPMENT 2019年第5期13卷 557-566页

作者： Beldar, Pedram Framinan, Jose M. Ardakani, Allahyar Islamic Azad Univ Dept Ind & Mech Engn Qazvin Branch Qazvin Iran Univ Seville Ind Management Sch Engn Camino Descubrimientos Seville 41092 Spain Univ Tasmania Natl Ctr Ports & Shipping Australian Maritime Coll Hobart Tas Australia

In this research study, a single machine batch-processing problem with release dates to minimize the total completion times of jobs is considered. The machine is able to process at most a certain number of jobs at the same time and the total size of the jobs allocated to a batch cannot exceed the machine capacity. Since the research problem has been shown to be NP-hard, an effective teaching-learning based optimization (TLBO) is proposed. A constructive heuristic approach is developed to generate initial feasible solutions for the TLBO. In order to enhance the efficiency of the proposed TLBO, a Tabu Search (TS) with three different neighborhood generation mechanisms is incorporated into the teaching phase and learner phase separately. To validate the outcomes of the proposed TLBO, we carry out an experimental study and compare its outcomes with the best-known results obtained by several meta-heuristic methods on a set of benchmark instances derived from the literature. The computational results show that the proposed TLBO with the incorporation of TS in its learning phase is able to come up with very good quality solutions.

关键词： Total completion times minimization Batch processing Single machine scheduling teaching-learning based optimization Heuristic approach

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Optimal incorporation of solar and wind DGs along with seasonal loads using teaching-learning based optimization 5

Optimal incorporation of solar and wind DGs along with seaso...

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IEEE 5th Conference on Knowledge based Engineering and Innovation (KBEI)

作者： Kani, Farzad Fallah Arbani, Mohammad Dameshghi Esmaeli, Abdolreza Univ Maziar Dept Elect Engn Noor Iran Azad Univ Dept Elect Engn Tafresh Iran Nucl Sci & Technol Res Inst Plasma & Nucl Fus Res Sch Tehran Iran

ISBN: (纸本)9781728108728

Renewable energy attracts more attention every day, because they are non-polluting and lasting. Recently, a significant number of renewable energy generating units (DGs) that are based on intermittent generation patterns are being added to the distribution network, in addition to the growing need for load, environmental threats are As well as reach the minimum. An appropriate combination of distributed disposable generation in distribution networks is important to guarantee the operational benefits of network quality. In this paper, the optimal location of solar sources and wind sources in a distribution network, taking into account seasonal loads and atmospheric changes has been made. Modeling has been made due to uncertainty in solar beam and wind speed. For better stability and improvement of power quality, we consider constraints on the voltage and current flow of transmission lines and the problem of optimizing the location of resources with these constraints has become a multi-objective optimization problem. To solve the problem, the optimization method teaching-learning based optimization (TLBO) has been used. The proposed method has been tested and tested on a standard 30-bus network. According to simulation results, the proposed method has had satisfactory and promising results.

关键词： distributed generation solar-wind hybrid system teaching-learning based optimization

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An application of teaching-learning-based optimization for solving the optimal power flow problem with stochastic wind and solar power generators

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RESULTS IN CONTROL AND optimization 2023年 10卷

作者： Sulaiman, Mohd Herwan Mustaffa, Zuriani Rashid, Muhammad Ikram Mohd Univ Malaysia Pahang Fac Elect & Elect Engn Technol Pekan 26600 Pahang Malaysia Univ Malaysia Pahang Fac Comp Pekan 26600 Pahang Malaysia

This paper proposes the implementation of metaheuristic algorithm namely, teaching-learningbased optimization (TLBO) algorithm to solve optimal power flow (OPF) problem. TLBO is inspired by philosophy of teaching and learning in the classroom. OPF on the other hand, is one of the most complex problems in power system operation, where in this paper, two objective functions aimed to be minimized by TLBO namely cost minimization and combined cost and emission (CEE) minimization. The effectiveness of proposed TLBO in solving the OPF is tested on modified IEEE -57 bus system that integrated with stochastic wind and solar power generations. To show the effectiveness of the proposed TLBO, several recent algorithms that have been proposed in literature will be utilized and compared. The simulations demonstrate the superiority of TLBO as an effective alternative solution for the OPF problems, where for the cost minimization, TLBO able to obtained 0.16% cost saving per hour compared to the second best algorithm;and for the CEE minimization, TLBO outperformed the second best algorithm by 0.12% cost saving per hour.

关键词： Cost and emission minimizations Metaheuristic algorithms Optimal power flow teaching-learning based optimization Stochastic power generations

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An Improved teaching-learning based optimization Algorithm and Its Application to Aero-Engine Start Model Adaptation

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IEEE ACCESS 2019年 7卷 136525-136534页

作者： Feng, Hailong Li, Qiuhong Nanjing Univ Aeronaut & Astronaut Jiangsu Prov Key Lab Aerosp Power Syst Nanjing 210016 Peoples R China

The modeling of the engine starting process is vital to ensure the successful start of the engine. However, the engine starting process is very complicated and challenging to model. To optimize the start model performance, an improved teaching-learning based optimization (ITLBO) algorithm is proposed. In ITLBO, a collective lesson preparation phase is increased to enhance the teaching ability of the teacher. The random learning phase is replaced by S-shape group learning, and students learn from the top students of their groups. Also the deterministic sampling selection phase is introduced to ITLBO, and the students with higher evaluation have more possibility to advance in class. The improved algorithm is tested on 18 benchmark functions. The results indicate that the proposed ITLBO algorithm performs much better in terms of convergence speed and accuracy than standard TLBO. When applied to the model adaptation of the turbofan engine starting process, ITLBO is used to optimize the speed line of the rotation components gradually from the lower speed line to the idle speed line. The weighted sum of relative errors between the model outputs and the start test data is taken as the fitness function. After adaptation, the maximum relative errors of model outputs to start test data are significantly decreased, which shows the effectiveness of the ITLBO in model adaption.

关键词： Education Adaptation models optimization Engines Heuristic algorithms Convergence Sociology Model adaptation teaching-learning based optimization collective lesson preparation turbofan engine

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