Constrained optimization based on improved teaching–learning-based optimization algorithm

Yu, Kunjie; Wang, Xin; Wang, Zhenlei

doi:10.1016/j.ins.2016.02.054

Cited by 56 publications

(18 citation statements)

References 61 publications

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“…ICTLBO [142] (Improved constrained teaching-learningbased optimization) is proposed for the constrained optimization problem. In teacher phase, according to the Euclidean distance, the population P is partitioned into k subpopulation, each size of subpopulation is NS.…”

Section: (T Fmentioning

confidence: 99%

A Survey of Application and Classification on Teaching-Learning-Based Optimization Algorithm

Ren

2020

IEEE Access

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Teaching-Learning-based Optimization is an optimization technique which does not require any algorithm-specific parameters and is popular for its less computational cost and high consistency. Therefore, it has achieved great success application by the researchers in various disciplines of engineering. It works on the philosophy of teaching and learning which is used to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. Recently the basic TLBO algorithm is improved to enhance its exploration and exploitation capacities and the performance. However, there is less surveys on TLBO algorithm recent advances and its application. In this paper, the successful researches of TLBO algorithm of the past decade are surveyed. Firstly, the available intelligent optimization algorithms were reviewed. Then the application fields of TLBO and the improved TLBO were discussed and analyzed. Furthermore, some representative TLBO methods were classified into three main groups: 1) Improvement of teaching process; and 2) Fusion with Other Optimization Methods; and 3) Weight Methods and Others. Finally, our viewpoints were shared on the open issues and challenges in TLBO as well as research trends in the future.

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Section: (T Fmentioning

confidence: 99%

A Survey of Application and Classification on Teaching-Learning-Based Optimization Algorithm

Ren

2020

IEEE Access

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“…(3) Initialize learners and evaluate them; (4) while stopping condition is not met (5) Choose the best learner as x teacher ; (6) Calculate the mean x mean of all learners; (7) for each learner x (8) // Teacher phase // (9) T F = round(1 + rand(0, 1)); (10) Update the learner according to Eq. (1); (11) Evaluate the new learner x ,new ; (12) Accept x ,new if it is better than the old one x ,old (13) // Learner phase // (14) Randomly select another learner x which is different from x ; (15) Update the learner according to Eq. (2); (16) Evaluate the new learner x ,new ; (17) Accept x ,new if it is better than the old one x ,old ; (18) end for (19) The second category is the hybrid TLBO method by combining it with other search strategies.…”

Section: Improvements On Tlbomentioning

confidence: 99%

“…TLBO utilizes two productive operators, namely, teacher phase and learning phase to search good solutions [12]. Due to its attractive characters such as simple concept, without the specific algorithm parameters, easy implementation, and rapid convergence, TLBO has captured great attention and has been extended to handle constrained [13], multiobjective [14], large-scale [15], and dynamic optimization problems [16]. Furthermore, TLBO has also been successfully applied to many scientific and engineering fields, such as neural network training [17], power system dispatch [18], and production scheduling [19].…”

Section: Introductionmentioning

confidence: 99%

Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

et al. 2018

Journal of Applied Mathematics

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Teaching-learning-based optimization (TLBO) is a population-based metaheuristic search algorithm inspired by the teaching and learning process in a classroom. It has been successfully applied to many scientific and engineering applications in the past few years. In the basic TLBO and most of its variants, all the learners have the same probability of getting knowledge from others. However, in the real world, learners are different, and each learner’s learning enthusiasm is not the same, resulting in different probabilities of acquiring knowledge. Motivated by this phenomenon, this study introduces a learning enthusiasm mechanism into the basic TLBO and proposes a learning enthusiasm based TLBO (LebTLBO). In the LebTLBO, learners with good grades have high learning enthusiasm, and they have large probabilities of acquiring knowledge from others; by contrast, learners with bad grades have low learning enthusiasm, and they have relative small probabilities of acquiring knowledge from others. In addition, a poor student tutoring phase is introduced to improve the quality of the poor learners. The proposed method is evaluated on the CEC2014 benchmark functions, and the computational results demonstrate that it offers promising results compared with other efficient TLBO and non-TLBO algorithms. Finally, LebTLBO is applied to solve three optimal control problems in chemical engineering, and the competitive results show its potential for real-world problems.

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“…At the same time, because of the large number of existing TLBO algorithms and their variants in the public domain, TLBO is a feasible and promising approach for complex optimization problems. Hence, TLBO has been extended to many complex optimization problems such as large scale non-linear optimization problems [52], multi-objective problems [17], economic dispatch [4], and clustering problems [39].…”

Section: Motivationsmentioning

confidence: 99%

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

Chen

Zou

et al. 2016

Information Sciences

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Constrained optimization based on improved teaching–learning-based optimization algorithm

Cited by 56 publications

References 61 publications

A Survey of Application and Classification on Teaching-Learning-Based Optimization Algorithm

A Survey of Application and Classification on Teaching-Learning-Based Optimization Algorithm

Teaching-Learning-Based Optimization with Learning Enthusiasm Mechanism and Its Application in Chemical Engineering

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

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