Parameters estimation of photovoltaic (PV) model based on experimental data plays an important role in the simulation, evaluation, control, and optimization of PV systems. In the past decade, many metaheuristic algorithms have been used to extract the PV parameters; however, developing hybrid algorithms based on two or more metaheuristic algorithms may further improve the accuracy and reliability of single metaheuristic algorithms. In this paper, by combining teaching-learning-based optimization (TLBO) and artificial bee colony (ABC), we propose a new hybrid teaching-learning-based artificial bee colony (TLABC) for the solar PV parameter estimation problems. The proposed TLABC employs three hybrid search phases, namely teaching-based employed bee phase, learning-based on looker bee phase, and generalized oppositional scout bee phase to efficiently search the optimization parameters. TLABC is applied to identify parameters of different PV models, including single diode, double diode, and PV module, and the results of TLABC are compared with well-established TLBO and ABC algorithms, as well as those results reported in the literature. Experimental results show that TLABC can achieve superior performance in terms of accuracy and reliability for different PV parameter estimation problems.