###### Abstract
The authors concluded that the model represented reality with high accuracy and showed a good predictive capability. Once again, it can be seen that the approach followed by the authors was tedious and did not make use of machine learning. [PERSON] et al. [16] analyzed, from a thermodynamics point of view, the energy, exergy, and economics of an integrated solar combined cycle (ISCC) power plant situated in Algeria. It was made of two gas turbines of 40 MW each, one steam turbine of 80 MW, which was fed by two HRSGs (heat recovery steam generators), one solar steam generator (SSG), and a solar field of a total area of 183.120 m\({}^{2}\) and comprising 224 parabolic trough collectors (PTC). The plant operates as ISCC-PTC during sunny times and as a conventional combined cycle plant at other times. Hence, the SSG works as a boiler in parallel to the HRSG to increase the steam quantity. They found that energy and exergy efficiencies were, respectively, 56.0% and 53.29%. The levelized cost of energy (LCOE) was promising but still higher than a simple combined cycle. The ISCC-PTC power plant allowed some savings in natural gas consumption and CO\({}_{2}\) emission taxes. Many other researchers [17; 18; 19; 20; 21] have adopted similar thermodynamic analysis approaches to assess the performance of the integrated solar combined cycle power plant, even in recent years. Some ANFIS-based approaches have recently been investigated in other applications. For example, [PERSON] et al. [22] used a deep neural network (DNN) and hybrid models of ANFIS and metaheuristic optimization algorithms for the prediction of rock strain. For ANFIS training, they used four optimization algorithms, namely grey wolf optimizer (GWO), fireflies algorithm (FF), particle swarm optimization (PSO), and genetic algorithm (GA). They used a rank index to determine the most robust model, and for the data analyzed, concluded that DNN was the best model. [PERSON] et al. [23] employed ANFIS-PSO, ANFIS-GA, and ANFIS-BBO (biogeography-based optimization) to identify groundwater salinization in the coastal region of eastern Saudi Arabia. Their simulated results showed that the ANFIS-PSO algorithm had the highest accuracy (99%), followed by the ANFIS-GA and ANFIS-BBO. [PERSON] et al. [24] conducted a study on modeling air pollution by integrating ANFIS and three metaheuristic algorithms, which were GA, PSO, and DE (differential evolution). They compared the results of these methods to those of classical ANFIS and found that these methods were more successful than classical ANFIS for modeling and predicting air pollution. To find an efficient and reliable streamflow forecasting model, [PERSON] et al. [25] developed an ANFIS model coupled with an ant colony optimization (ACO) algorithm to predict the streamflow of a river for 1 day, 2 days, and 3 days ahead. They found that the accuracy of the simple ANFIS model obtained was good. However, the hybridization of ANFIS with the ACO algorithm significantly improved the streamflow prediction accuracies by 12.1, 12.91, and 13.66% in comparison with a simple ANFIS model. From the existing literature, and to our best knowledge, it appears that some authors have tried to model the performance of hybrid thermal power plants by means of ANFIS, but the hybridization of ANFIS with evolutionary algorithms (PSO and GA) has not been applied or investigated on the performance of hybrid thermal power plant (gas/steam/solar) systems yet. The aim of this study is to investigate and demonstrate the capability of metaheuristic methods (PSO and GA) combined with ANFIS to accurately predict the performance of a hybrid solar/gas/steam power plant or an integrated solar combined cycle power plant.