Teaching-Learning-Studying-Based Optimization (TLSBO)

版本 1.0.0 (3.5 KB) 作者: Ebrahim Akbari

TLSBO is an improved version of Teaching-Learning-Based Optimization Algorithm (TLBO) introduced in a 2021 paper

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The teaching-learning-based optimizer (TLBO) algorithm is a powerful and efficient optimization algorithm. However it is prone to getting stuck in local optima.

In the newly proposed teaching-learning-studying-based optimizer (TLSBO), the global optimization performance of TLBO was enhanced by adding a new strategy to TLBO, named studying strategy, in which each member uses the information from another randomly selected individual for improving its position.

The performance of TLSBO for solving different standard real-parameter benchmark functions and also various types of nonlinear optimal power flow (OPF) problems was investigated in the reference paper, whose results prove that TLSBO has faster convergence, higher quality for final optimal solution, and more power for escaping from convergence to local optima compared to original TLBO.

The reference paper: http://dx.doi.org/10.1080/15325008.2021.1971331

Every author at Taylor & Francis (including all co-authors) gets 50 free online copies of their article to share with friends and colleagues as soon as their article is published. My eprint link is now ready to use and is: https://www.tandfonline.com/eprint/VAGEKC9YPJMAAXD8CNS4/full?target=10.1080/15325008.2021.1971331

引用格式

Ebrahim Akbari (2024). Teaching-Learning-Studying-Based Optimization (TLSBO) (https://www.mathworks.com/matlabcentral/fileexchange/101348-teaching-learning-studying-based-optimization-tlsbo), MATLAB Central File Exchange. 检索来源 2024/4/29.

Ebrahim Akbari, Mojtaba Ghasemi, Milad Gil, Abolfazl Rahimnejad, and S. Andrew Gadsden. "Optimal Power Flow via Teaching-Learning-Studying-Based Optimization Algorithm." Electric Power Components and Systems, October 2021, pp. 1-18. doi:10.1080/15325008.2021.1971331