Group Leader Dominated Teaching-Learning Based Optimization

Chen, Chang-Huang

doi:10.1109/pdcat.2013.54

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…A modified mutation operator and a new teacher factor alleviate some drawbacks of the standard TLBO by avoiding premature convergence on a local optimum. In [32], a group discussion is introduced as a powerful method to increase the efficiency of standard TLBO by involving a group of leaders. Another approach to enhance the abilities of the standard TLBO is to boost the learning phase by adding a self-learning phase [33].…”

Section: Literature Reviewmentioning

confidence: 99%

A New Design Method for Optimal Parameters Setting of PSSs and SVC Damping Controllers to Alleviate Power System Stability Problem

et al. 2021

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This paper presents an improved Teaching-Learning-Based Optimization (TLBO) for optimal tuning of power system stabilizers (PSSs) and static VAR compensator (SVC)-based controllers. The original TLBO is characterized by easy implementation and is mainly free of control parameters. Unfortunately, TLBO may suffer from population diversity losses in some cases, leading to local optimum and premature convergence. In this study, three approaches are considered for improving the original TLBO (i) randomness improvement, (ii) three new mutation strategies (iii) hyperchaotic perturbation strategy. In the first approach, all random numbers in the original TLBO are substituted by the hyperchaotic map sequence to boost exploration capability. In the second approach, three mutations are carried out to explore a new promising search space. The obtained solution is further improved in the third strategy by implementing a new perturbation equation. The proposed HTLBO was evaluated with 26 test functions. The obtained results show that HTLBO outperforms the TBLO algorithm and some state-of-the-art algorithms in robustness and accuracy in almost all experiments. Moreover, the efficacy of the proposed HTLBO is justified by involving it in the power system stability problem. The results consist of the Integral of Absolute Error (ITAE) and eigenvalue analysis of electromechanical modes demonstrate the superiority and the potential of the proposed HTLBO based PSSs and SVC controllers over a wide range of operating conditions. Besides, the advantage of the proposed coordination design controllers was confirmed by comparing them to PSSs and SVC tuned individually.

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Section: Literature Reviewmentioning

confidence: 99%

A New Design Method for Optimal Parameters Setting of PSSs and SVC Damping Controllers to Alleviate Power System Stability Problem

et al. 2021

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“…The literature proposed a hybrid search algorithm named HSTLBO, in which HS mainly aims to explore unknown regions. In contrast, TLBO aims to rapidly develop high-accuracy solutions in known areas [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

Blockchain-Based Optimization Model for Evaluating Psychological Mental Disease and Mental Fitness

Prasad

Athawale

Raut

et al. 2022

Computational Intelligence and Neuroscience

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The current work describes a blockchain-based optimization approach that mimics the psychological mental illness evaluation procedure and evaluates mental fitness. Combining lightweight models with blockchains can give a variety of benefits in the healthcare business. This study aims to offer an improved review and learning optimization technique (SPLBO) based on the social psychology theory to overcome the biogeography-based optimization (BBO) algorithm’s shortcomings of low optimization accuracy and instability. It also creates high-accuracy solutions in recognized domains quickly. To retain student individuality, students can be divided into two groups: Human psychological variables are incorporated in the algorithm’s improvement: in the “teaching” step of the original BBO algorithm; the “expectation effect” theory of social psychology is combined: “field-independent” and “field-dependent” cognitive styles. As a consequence, low-weight deep neural networks have been designed in such a manner that they require fewer resources for optimal design while also improving quality. A responsive student update component is also introduced to duplicate the effect of the environment on students’ learning efficiency, increase the method’s global search capabilities, and avoid the problem of falling into a local optimum in the first repetition.

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“…In short span of time, this algorithm become more popular among researchers and has been applied to solve variety of problems. A lot of optimization problems have been solved by using TLBO algorithm and provides better results in comparison to existing algorithms [20][21][22][23][24]. Still, there are several shortcomings that can affect the performance of TLBO algorithm such as quality of solution, stuck in local optima when solving global optimization problems, premature convergence, tradeoff between searching capability and local search ability.…”

Section: Introductionmentioning

confidence: 99%

A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

Kumar

Dahiya²,

Malik³

et al. 2019

IJCAI

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This paper presents a new variant of teaching learning based optimization (TLBO) algorithm for solving global optimization problems. The performance of the TLBO algorithm depends on coordination of teacher phase and learner phase. It is noticed that sometimes performance of TLBO algorithm is affected due to lack of diversity in teacher and learner phases. In this work, a new variant of TLBO algorithm is proposed based on genetic crossover and mutation strategies. These strategies are inculcated in TLBO algorithm for improving its search mechanism and convergence rate. Genetic mutation strategy is applied in teacher phase of TLBO algorithm for improving the mean knowledge of leaners. While, Crossover strategy is applied in learner phase of TLBO algorithm to find the good learner. The effectiveness of the proposed algorithm is tested on several bench mark test functions of CEC'14. From simulation results, it is stated that the proposed algorithm provides more optimized results in comparison to same class of algorithms.Povzetek: V prispevku je opisan nov globalni optimizacijski algoritem na osnovi učenja optimizacije (TLBO).

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Group Leader Dominated Teaching-Learning Based Optimization

Cited by 6 publications

References 8 publications

A New Design Method for Optimal Parameters Setting of PSSs and SVC Damping Controllers to Alleviate Power System Stability Problem

A New Design Method for Optimal Parameters Setting of PSSs and SVC Damping Controllers to Alleviate Power System Stability Problem

Blockchain-Based Optimization Model for Evaluating Psychological Mental Disease and Mental Fitness

A New Variant of Teaching Learning Based Optimization Algorithm for Global Optimization Problems

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