*3.7. TLBOs*

TLBO is a group metaheuristic developed based on the influence of teachers on students [115]. TLBO assumes that student outcomes are related to teacher competence. As the best in the group, the teacher teaches the students and raises the group's average achievement by a random factor. Students learn from each other at random coefficients during the learning phase and are led by the better of the two at random.

Chen et al. [116] suggested a generalized opposition-based learning mechanism for TLBO (GOTLBO). GOTLBO was demonstrated with excellent performance in benchmark functions and parameter extraction cases. To target different stages' effectiveness, Yu et al. [117] developed a self-adaptive TLBO (SATLBO) concerning elite learning mechanisms in the teacher stage and diverse learning mechanisms in the learner stage. SATLBO achieved competitive RMSE values in several PV models. Ramadan et al. [118] developed an enhanced TLBO (ETLBO) with controlled parameters replacing random parameter values and highlighted its effectiveness and competitiveness by extracting PV model parameters. Xiong et al. [21] developed an either/or TLBO (EOTLBO). To improve the generalizability of the method, EOTLBO replaced the mean with the learner median at the teacher stage. A random learner was added to the EOTLBO at the learner stage to improve the exploration capacity. The authors argued that it was inefficient for individuals to go through both teacher and learner stages, so EOTLBO implemented an either/or mechanism to choose one stage based on a chaotic map. EOTLBO showed excellent competitiveness, accuracy, and reliability. Abdel-Basset et al. [119] designed a modified TLBO (MTLBO). Individuals

in both stages were divided into three strata of ground performance. Individual updates within each stratum did not interfere with each other. MTLBO was demonstrated with high accuracy in five PV models. Li et al. [120] developed an optimized TLBO (DMTLBO). The authors introduced the idea of dynamic self-adaption to the teacher stage and the idea of inter-comparison to the learner stage to further explore the capabilities of each stage. DMTLBO's accuracy, speed, and competitiveness were confirmed in different cases.

The essential information and experimental results of the TLBO variants are summarized in Tables 12 and 13. In the crucial information, GOTLBO has the least computational resources, followed by EOTLBO, SATLBO, MTLBO, DMTLBO, and ETLBO. In the accuracy ranking, EOTLBO comes first, followed by DMTLBO, MTLBO, and SATLBO. GOTLBO and ETLBO are not included because of missing values for some of the selected cases in the ranking. A direct comparison of the values in Table 13 reveals that the MIN RMSE of GOTLBO and ETLBO, which are early variants, struggle to outperform the other TLBO variants of recent years. An upward trend in the improvement of TLBO can be observed. However, the consumption of computational resources, unlike the development of accuracy, does not decrease significantly with the approaching number of years. Therefore, a reduction in the use of computational resources needs to be considered in future studies of TLBO.

**Method Main Contributors Case Algorithmic Parameter Indicator TNFES Run** GOTLBO [116] Chen et al., School of Electrical and Information Engineering, Jiangsu University SDM *NP* = 20, SDM: *Jr* = 0.1, DDM: *Jr* = 0 RMSE 10,000 <sup>30</sup> DDM 20,000 SATLBO [117] Yu et al., Key Laboratory of Advanced Control and Optimization for Chemical Processes, Ministry of Education, East China University of Science and Technology SDM *NP* = 40 RMSE 50,000 <sup>30</sup> DDM Photowatt-PWP201 ETLBO [118] Ramadan et al., Department of Electrical Engineering, Faculty of Engineering, Aswan University SDM *NP* = 200, Iteration = 5000, RMSE IAE - - DDM STM6-40/36 STP6-120/36 EOTLBO [21] Xiong et al., Guizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University SDM *NP* = 50 RMSE WRT FT 20,000 <sup>50</sup> DDM Photowatt-PWP201 Sharp ND-R250A5 MTLBO [119] Abdel-Basset et al., Faculty of Computers and Informatics, Zagazig University SDM *NP* = 50 RMSE 50,000 30 DDM Photowatt-PWP201 STM6-40/36 STP6-120/36 DMTLBO [120] Li et al., Guizhou Key Laboratory of Intelligent Technology in Power System, College of Electrical Engineering, Guizhou University SDM *NP* = 50 RMSE SIAE 50,000 <sup>30</sup> DDM Photowatt-PWP201 STM6-40/36 STP6-120/36

**Table 12.** TLBOs' essential information and metrics.


**Table 13.** TLBOs' experiment results.

The "N/A" means that there is insufficient data to support an average algorithm ranking using the Friedman Test on the three cases: SDM, DDM, and Photowatt-PWP201.
