A Survey of Application and Classification on Teaching-Learning-Based Optimization Algorithm

Abstract

Teaching-Learning-based Optimization is an optimization technique which does not require any algorithm-specific parameters and is popular for its less computational cost and high consistency. Therefore, it has achieved great success application by the researchers in various disciplines of engineering. It works on the philosophy of teaching and learning which is used to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. Recently the basic TLBO algorithm is improved to enhance its exploration and exploitation capacities and the performance. However, there is less surveys on TLBO algorithm recent advances and its application. In this paper, the successful researches of TLBO algorithm of the past decade are surveyed. Firstly, the available intelligent optimization algorithms were reviewed. Then the application fields of TLBO and the improved TLBO were discussed and analyzed. Furthermore, some representative TLBO methods were classified into three main groups: 1) Improvement of teaching process; and 2) Fusion with Other Optimization Methods; and 3) Weight Methods and Others. Finally, our viewpoints were shared on the open issues and challenges in TLBO as well as research trends in the future.

A Multi-Objective Teaching–Learning Studying-Based Algorithm for Large-Scale Dispatching of Combined Electrical Power and Heat Energies

“…TLBA has caught considerable interest due to various qualities involving its simple concept, absence of algorithm-specific constants, speedy convergence and simplicity of application [27]. The TLBA has been previously applied in an efficient way for several engineering optimization problems [28]. Some examples of these successful implementations are reactive power control in electrical systems [29], service restoration in distribution feeders [30], Tsallis-entropy-based feature selection classification [31], generation expansion-planning problem [32], design of passive filters [33], dissimilar resistance spot-welding process [34], water supply pipe condition prediction [35], robot manipulator calibration [36], harmonic elimination in multi-level inverters [37], operation analysis of a grid-connected photovoltaic (PV) with battery system [38] and parameter extraction of PV modules [39,40].…”

Performance up-gradation of Symbiotic Organisms Search by Backtracking Search Algorithm

“…In order to have a fair comparison of the proposed e-SOSBSA to state-of-the-art algorithms, e-SOSBSA, JAYA (Kumar and Mishra 2018), TLBO (Rao et al 2011;Kumar and Mishra 2017;Xue and Wu 2020), TSA (Kaur et al 2020), SOA (Dhiman and Kumar 2019), CSA (Khishe and Mosavi 2020), SHO (Dhiman and Kumar 2017), and EO (Faramarzi et al 2020) algorithms are used. All these algorithms are highly competitive, especially with the newest ones from 2019 and 2020,and have proved their worth in various CEC competitions and solving other real-world optimization problems.…”

Realistic Optimal Power Flow of a Wind-Connected Power System With Enhanced Wind Speed Model

“…Two efficient and robust optimization algorithms are adopted to solve the proposed OPF models: teachinglearning-based optimization (TLBO) and symbiotic organisms search (SOS). These algorithms have been recently introduced as powerful stochastic optimization algorithms [14]− [20]. Most nature-inspired metaheuristic algorithms rely on random variables and have some parameters to be fitted to handle the problem under study [16]- [18].…”