Research on the fabrication of organic field effect transistors (OFETs) has been dramatically increased in the last decade, considering their lightweight and flexible structure as well as their practical and low-cost production. In the next step of fabrication, building compact models and developing parameter extraction methods have critical importance in designing electronic circuits using fabricated OFETs. In this paper, we propose a parameter extraction approach which benefits from the power of metaheuristics. Although direct extraction tools offer analytical solutions in successive steps to extract model parameters, metaheuristics-based global optimization methods can find all parameters at once by exploring a wide range of parameters. Direct extraction tools are cumbersome and need human expertise. On the other hand, global optimization methods are very flexible, adaptive and can be automated as parameter extraction tools of any compact model. In this study, we introduce three different global optimization algorithms, namely, a genetic algorithm (GA), a hybrid artificial bee colony (h-ABC) algorithm, and bacterial foraging optimization (BFO) algorithm, for the parameter extraction. To the best of our knowledge, h-ABC and BFO algorithms have been used for the first time in extracting parameters of OFET compact models. We use two OFET compact models developed by Estrada et al. and Marinov et al. for two different datasets of OFET transistors, both having pentacene as organic semiconductor. While one of the dataset of transistor (T1) is available in literature, the dataset of the other transistor fabricated in our laboratory (T2) is generated as a new dataset. In order to tune control parameters of the developed algorithms, Taguchi's orthogonal experimental design (OED) method is used. Experimental results show that the proposed metaheuristics-based approach can extract model parameters successfully and can perform better than direct extraction methods. The studied OFET compact models fit to the experimental data with these parameters and predict similar output characteristic curves. The algorithms show a good agreement with the experimental data of T1 and T2, having normalized root-mean-square error values under 3.70%, and 8.74% for the models of Estrada et al. and Marinov et al., respectively. It is shown that h-ABC and BFO algorithms perform better than GA on average. It is also observed that the compact model by Estrada et al. performs better for both T1 and T2 compared to the model of Marinov et al.