Teaching-learning-based optimization with a fuzzy grouping learning strategy for global numerical optimization

Recently, teaching-learning-based optimization (TLBO), as one of the emerging nature-inspired heuristic algorithms, has attracted increasing attention. In order to enhance its convergence rate and prevent it from getting stuck in local optima, a novel metaheuristic has been developed in this paper, where particular characteristics of the chaos mechanism and Lévy flight are introduced to the basic framework of TLBO. The new algorithm is tested on several large-scale nonlinear benchmark functions with different characteristics and compared with other methods. Experimental results show that the proposed algorithm outperforms other algorithms and achieves a satisfactory improvement over TLBO.

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Improved Teaching-Learning-based Optimization Algorithm with Advanced Learning Strategy

2020 12th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) ◽

10.1109/ihmsc49165.2020.00010 ◽

2020 ◽

Author(s):

Jiazhen Ye ◽

Yang Yu ◽

Yu Zhang ◽

Yuki Todo ◽

Shangce Gao

Keyword(s):

Optimization Algorithm ◽

Learning Strategy ◽

Teaching Learning Based Optimization ◽

Teaching Learning

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Bare-Bones Teaching-Learning-Based Optimization

The Scientific World JOURNAL ◽

10.1155/2014/136920 ◽

2014 ◽

Vol 2014 ◽

pp. 1-17 ◽

Cited By ~ 12

Author(s):

Feng Zou ◽

Lei Wang ◽

Xinhong Hei ◽

Debao Chen ◽

Qiaoyong Jiang ◽

...

Keyword(s):

Learning Strategy ◽

Optimization Problems ◽

Neighborhood Search ◽

Tlbo Algorithm ◽

Teaching Learning Based Optimization ◽

Class Room ◽

Teaching Learning ◽

Bare Bones ◽

Gaussian Sampling ◽

Better Than

Teaching-learning-based optimization (TLBO) algorithm which simulates the teaching-learning process of the class room is one of the recently proposed swarm intelligent (SI) algorithms. In this paper, a new TLBO variant called bare-bones teaching-learning-based optimization (BBTLBO) is presented to solve the global optimization problems. In this method, each learner of teacher phase employs an interactive learning strategy, which is the hybridization of the learning strategy of teacher phase in the standard TLBO and Gaussian sampling learning based on neighborhood search, and each learner of learner phase employs the learning strategy of learner phase in the standard TLBO or the new neighborhood search strategy. To verify the performance of our approaches, 20 benchmark functions and two real-world problems are utilized. Conducted experiments can been observed that the BBTLBO performs significantly better than, or at least comparable to, TLBO and some existing bare-bones algorithms. The results indicate that the proposed algorithm is competitive to some other optimization algorithms.

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Strengthened Teaching–Learning-Based Optimization Algorithm for Numerical Optimization Tasks

10.21203/rs.3.rs-696824/v1 ◽

2021 ◽

Author(s):

Xuefen Chen ◽

Chunming Ye ◽

Yang Zhang ◽

Lingwei Zhao ◽

Jing Guo ◽

...

Keyword(s):

Optimization Algorithm ◽

Numerical Optimization ◽

Optimization Techniques ◽

Data Availability ◽

Search Performance ◽

Local Optima ◽

Cauchy Mutation ◽

Teaching Learning Based Optimization ◽

Teaching Learning ◽

Solution Accuracy

Abstract The teaching–learning-based optimization algorithm (TLBO) is an efficient optimizer. However, it has several shortcomings such as premature convergence and stagnation at local optima. In this paper, the strengthened teaching–learning-based optimization algorithm (STLBO) is proposed to enhance the basic TLBO’s exploration and exploitation properties by introducing three strengthening mechanisms: the linear increasing teaching factor, the elite system composed of new teacher and class leader, and the Cauchy mutation. Subsequently, seven variants of STLBO are designed based on the combined deployment of the three improved mechanisms. Performance of the novel STLBOs is evaluated by implementing them on thirteen numerical optimization tasks. The results show that STLBO7 is at the top of the list, significantly better than the original TLBO. Moreover, the remaining six variants of STLBO also outperform TLBO. Finally, a set of comparisons are implemented between STLBO7 and other advanced optimization techniques. The numerical results and convergence curves prove that STLBO7 clearly outperforms other competitors, has stronger local optimal avoidance, faster convergence speed and higher solution accuracy. All the above manifests that STLBOs has improved the search performance of TLBO. Data Availability Statements: All data generated or analyzed during this study are included in this published article.

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Modified teaching-learning-based optimization algorithm for multi-objective optimization problems

Journal of Intelligent & Fuzzy Systems ◽

10.3233/jifs-212743 ◽

2022 ◽

pp. 1-10

Author(s):

Zhi Wang ◽

Shufang Song ◽

Hongkui Wei

Keyword(s):

Optimization Algorithm ◽

Learning Strategy ◽

Optimization Problems ◽

Teaching Strategy ◽

Multi Objective Optimization ◽

Aerodynamic Optimization ◽

Multi Objective ◽

Teaching Learning Based Optimization ◽

Benchmark Test Functions ◽

Teaching Learning

When solving multi-objective optimization problems, an important issue is how to promote convergence and distribution simultaneously. To address the above issue, a novel optimization algorithm, named as multi-objective modified teaching-learning-based optimization (MOMTLBO), is proposed. Firstly, a grouping teaching strategy based on pareto dominance relationship is proposed to strengthen the convergence efficiency. Afterward, a diversified learning strategy is presented to enhance the distribution. Meanwhile, differential operations are incorporated to the proposed algorithm. By the above process, the search ability of the algorithm can be encouraged. Additionally, a set of well-known benchmark test functions including ten complex problems proposed for CEC2009 is used to verify the performance of the proposed algorithm. The results show that MOMTLBO exhibits competitive performance against other comparison algorithms. Finally, the proposed algorithm is applied to the aerodynamic optimization of airfoils.

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An Improved Teaching-Learning-Based Optimization with Differential Learning and Its Application

Mathematical Problems in Engineering ◽

10.1155/2015/754562 ◽

2015 ◽

Vol 2015 ◽

pp. 1-19 ◽

Cited By ~ 4

Author(s):

Feng Zou ◽

Lei Wang ◽

Debao Chen ◽

Xinhong Hei

Keyword(s):

Learning Strategy ◽

Population Based ◽

Neighborhood Search ◽

New Mutation ◽

Iir Filter ◽

Differential Learning ◽

Tlbo Algorithm ◽

Teaching Learning Based Optimization ◽

Digital Iir Filter ◽

Teaching Learning

The teaching-learning-based optimization (TLBO) algorithm is a population-based optimization algorithm which is based on the effect of the influence of a teacher on the output of learners in a class. A variant of teaching-learning-based optimization (TLBO) algorithm with differential learning (DLTLBO) is proposed in the paper. In this method, DLTLBO utilizes a learning strategy based on neighborhood search of teacher phase in the standard TLBO to generate a new mutation vector, while utilizing a differential learning to generate another new mutation vector. Then DLTLBO employs the crossover operation to generate new solutions so as to increase the diversity of the population. By the integration of the local search and the global search, DLTLBO achieves a tradeoff between exploration and exploitation. To demonstrate the effectiveness of our approaches, 24 benchmark functions are used for simulating and testing. Moreover, DLTLBO is used for parameter estimation of digital IIR filter and experimental results show that DLTLBO is superior or comparable to other given algorithms for the employed examples.

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