Balanced TLBO (BLBO) Matlab Source Code (CEC2014)

版本 1.0.0 (1.7 MB) 作者: Ahmad Taheri

An efficient Balanced Teaching-Learning-Based optimization algorithm with Individual restarting strategy

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Teaching-learning-based optimization (TLBO) is a population-based metaheuristic algorithm which simulates the teaching and learning mechanisms in a classroom. The TLBO algorithm has emerged as one of the most efficient and attractive optimization techniques. Even though the TLBO algorithm has an acceptable exploration capability and fast convergence speed, there may be a possibility to converge into a local optimum during solving complex optimization problems and there is a need to keep a balance between exploration and exploitation capabilities. Hence, a Balanced Teaching-Learning-Based Optimization (BTLBO) algorithm is proposed. The proposed BTLBO algorithm is a modification of the TLBO algorithm and it consists of four phases: (1) Teacher Phase in which a weighted mean is used instead of a mean value for keeping the diversity, (2) Learner Phase, which is same as the learner phase of basic TLBO algorithm, (3) Tutoring Phase, which is a powerful local search for exploiting the regions around the best ever found solution, and (4) Restarting Phase, which improves exploration capability by replacing inactive learners with new randomly initialized learners. An acceptable balance between the exploration and exploitation capabilities is achieved by the proposed BTLBO algorithm.

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Taheri, Ahmad, et al. “An Efficient Balanced Teaching-Learning-Based Optimization Algorithm with Individual Restarting Strategy for Solving Global Optimization Problems.” Information Sciences, vol. 576, Elsevier BV, Oct. 2021, pp. 68–104, doi:10.1016/j.ins.2021.06.064.

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BTLBO Matlab Source Code CEC2014

BTLBO Matlab Source Code CEC2014/input_data

Run_BTLBO.m

版本	已发布	发行说明
1.0.0	2023/11/3		下载

MLA	Taheri, Ahmad, et al. “An Efficient Balanced Teaching-Learning-Based Optimization Algorithm with Individual Restarting Strategy for Solving Global Optimization Problems.” Information Sciences, vol. 576, Elsevier BV, Oct. 2021, pp. 68–104, doi:10.1016/j.ins.2021.06.064.
APA	Taheri, A., RahimiZadeh, K., & Rao, R. V. (2021). An efficient Balanced Teaching-Learning-Based optimization algorithm with Individual restarting strategy for solving global optimization problems. Information Sciences, 576, 68–104. Elsevier BV. Retrieved from https://doi.org/10.1016%2Fj.ins.2021.06.064
BibTeX	@article{Taheri_2021, doi = {10.1016/j.ins.2021.06.064}, url = {https://doi.org/10.1016%2Fj.ins.2021.06.064}, year = 2021, month = {oct}, publisher = {Elsevier {BV}}, volume = {576}, pages = {68--104}, author = {Ahmad Taheri and Keyvan RahimiZadeh and Ravipudi Venkata Rao}, title = {An efficient Balanced Teaching-Learning-Based optimization algorithm with Individual restarting strategy for solving global optimization problems}, journal = {Information Sciences} }