Applications of TLBO Algorithm and Its Modifications to Different Engineering and Science Disciplines

Rao, R. Venkata

doi:10.1007/978-3-319-22732-0_15

Cited by 11 publications

(9 citation statements)

References 119 publications

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“…In order to verify the performance of MSCA, the experiment will be conducted from the following three aspects: (1) Contrast experiment is conducted between MSCA and particle swarm optimization (PSO) [ 8 ], differential evolution (DE) [ 9 ], bat algorithm (BA) [ 33 , 34 ], teaching-learning-based optimization (TLBO) [ 35 , 36 ], grey wolf optimizer (GWO) [ 37 ], and basic SCA algorithm. (2) The effectiveness of 3 improvement strategies is analyzed.…”

Section: Experimental Simulationmentioning

confidence: 99%

A Modified Sine-Cosine Algorithm Based on Neighborhood Search and Greedy Levy Mutation

Zeng

Dai

et al. 2018

Computational Intelligence and Neuroscience

104

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For the deficiency of the basic sine-cosine algorithm in dealing with global optimization problems such as the low solution precision and the slow convergence speed, a new improved sine-cosine algorithm is proposed in this paper. The improvement involves three optimization strategies. Firstly, the method of exponential decreasing conversion parameter and linear decreasing inertia weight is adopted to balance the global exploration and local development ability of the algorithm. Secondly, it uses the random individuals near the optimal individuals to replace the optimal individuals in the primary algorithm, which allows the algorithm to easily jump out of the local optimum and increases the search range effectively. Finally, the greedy Levy mutation strategy is used for the optimal individuals to enhance the local development ability of the algorithm. The experimental results show that the proposed algorithm can effectively avoid falling into the local optimum, and it has faster convergence speed and higher optimization accuracy.

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Section: Experimental Simulationmentioning

confidence: 99%

A Modified Sine-Cosine Algorithm Based on Neighborhood Search and Greedy Levy Mutation

Zeng

Dai

et al. 2018

Computational Intelligence and Neuroscience

104

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“…Teaching-Learning-Based Optimization algorithm was proposed for the first time by Rao et al in 2011 [ 39 ]. It is a metaheuristic algorithm inspired by process of teaching and learning through a simple mathematical model of knowledge amelioration gained by the students in the class [ 40 ].…”

Section: Evolutionary Trained Anfis Algorithmsmentioning

confidence: 99%

TLBO-Based Adaptive Neurofuzzy Controller for Mobile Robot Navigation in a Strange Environment

Aouf

Boussaid

Sakly

2018

Computational Intelligence and Neuroscience

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This work investigates the possibility of using a novel evolutionary based technique as a solution for the navigation problem of a mobile robot in a strange environment which is based on Teaching-Learning-Based Optimization. TLBO is employed to train the parameters of ANFIS structure for optimal trajectory and minimum travelling time to reach the goal. The obtained results using the suggested algorithm are validated by comparison with different results from other intelligent algorithms such as particle swarm optimization (PSO), invasive weed optimization (IWO), and biogeography-based optimization (BBO). At the end, the quality of the obtained results extracted from simulations affirms TLBO-based ANFIS as an efficient alternative method for solving the navigation problem of the mobile robot.

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“…Meta-heuristic methods are generally flexible in solving non-convex non-linear problems and naturally immune to the irregular problem formulations and constraints. Among many heuristic methods developed so far, teaching-learning-based optimization (TLBO) is a latest powerful method free of specific parameter tunings proposed by Rao et al [29] and has been applied in solving a number of single or multiple objectives industrial optimization problems [30,31]. The original TLBO and some efficient variants such as modified Teaching-learning based optimization (MTLBO) [32] and self-learning Teaching-learning based optimization (SL-TLBO) [ 33 ] are employed in this paper to solve the nonlinear, time-varying, complicated battery optimal charging problem.…”

Section: Teaching-learning Based Optimization and Its Variantsmentioning

confidence: 99%

An advanced Lithium-ion battery optimal charging strategy based on a coupled thermoelectric model

Liu

Yang

et al. 2017

Electrochimica Acta

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. (2017) An advanced Lithiumion battery optimal charging strategy based on a coupled thermoelectric model. Electrochimica Acta, 225. pp. 330-344. Permanent WRAP URL:http://wrap.warwick.ac.uk/86302 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP URL' above for details on accessing the published version and note that access may require a subscription. Kingdom (Email:{kliu02,k.li,zyang07,czhang07,j.deng}@qub.ac.uk).Abstract: Lithium-ion batteries are widely adopted as the power supplies for electric vehicles. A key but challenging issue is to achieve optimal battery charging, while taking into account of various constraints for safe, efficient and reliable operation. In this paper, a triple-objective function is first formulated for battery charging based on a coupled thermoelectric model. An advanced optimal charging strategy is then proposed to develop the optimal constant-current-constant-voltage (CCCV) charge current profile, which gives the best trade-off among three conflicting but important objectives for battery management. To be specific, a coupled thermoelectric battery model is first presented. Then, a specific triple-objective function consisting of three objectives, namely charging time, energy loss, and temperature rise (both the interior and surface), is proposed.Heuristic methods such as Teaching-learning-based-optimization (TLBO) and particle swarm optimization (PSO) are applied to optimize the triple-objective function, and their optimization performances are compared.The impacts of the weights for different terms in the objective function are then assessed. Experimental results show that the proposed optimal charging strategy is capable of offering desirable effective optimal charging current profiles and a proper trade-off among the conflicting objectives. Further, the proposed optimal charging strategy can be easily extended to other battery types.

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Applications of TLBO Algorithm and Its Modifications to Different Engineering and Science Disciplines

Cited by 11 publications

References 119 publications

A Modified Sine-Cosine Algorithm Based on Neighborhood Search and Greedy Levy Mutation

A Modified Sine-Cosine Algorithm Based on Neighborhood Search and Greedy Levy Mutation

TLBO-Based Adaptive Neurofuzzy Controller for Mobile Robot Navigation in a Strange Environment

An advanced Lithium-ion battery optimal charging strategy based on a coupled thermoelectric model

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