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

Ge, Fangzhen; Lü, Hong; Shi, Li

doi:10.1080/18756891.2016.1175815

Cited by 15 publications

(7 citation statements)

References 30 publications

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“…TR is a random number. In autonomous teaching-learning based optimization algorithm(ATLBO) [137], the teaching process is divided into three stages: teacher phase, mutual learning phase and selflearning phase. In teacher phase, the random number r is replaced by r • G i (t).…”

Section: A Improvement In Teaching Processmentioning

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: A Improvement In Teaching Processmentioning

confidence: 99%

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

Ren

2020

IEEE Access

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“…A hybrid TLBO is presented by Xie et al to deal with the permutation flow shop scheduling problem [27]. An autonomous TLBO is presented by Ge et al to deal with the global optimization problems on the continuous space [28]. Except for modifying the learning way of TLBO, researchers also improved TLBO by combining it with other evolutionary algorithms.…”

Section: Related Work About Tlbomentioning

confidence: 99%

An Improved Teaching-Learning Based Optimization Algorithm and Its Application to Aero-Engine Start Model Adaptation

Feng

2019

IEEE Access

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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. INDEX TERMS Model adaptation, teaching-learning based optimization, collective lesson preparation, turbofan engine.

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“…26 with good results. Though TLBO has been found to be good at exploration, it performs poorly in exploitation, resulting in premature convergence in complex problems 27 .…”

Section: Teaching-learning Based Optimizationmentioning

confidence: 99%

“…Since its design, ABO has been successfully applied to solve several optimization problems, such as the travelling salesman problem, strategic management 27 , numerical function optimization, parameter-tuning of PID controllers for automatic voltage regulators, etc. 28 .…”

Section: The African Buffalo Optimization Algorithmmentioning

confidence: 99%

African Buffalo Optimization for Global Optimization

Odili¹,

Ahmad

2018

Current Science

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In this study we apply the African buffalo optimization (ABO) to solve benchmark global optimization problems. Such problems which are artificial representation of different search landscapes ranging from unimodal to multimodal, separable to non-separable, constrained to unconstrained search landscapes have become a veritable instrument to test the search capacities of optimization algorithms. After a number of experimental procedures involving 28 benchmark problems, results from ABO prove to be rather competitive leading to the conclusion that it is a worthy addition to the body of swarm intelligence techniques.

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

Cited by 15 publications

References 30 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

An Improved Teaching-Learning Based Optimization Algorithm and Its Application to Aero-Engine Start Model Adaptation

African Buffalo Optimization for Global Optimization

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