Because of the strong heterogeneity and complex pore types of the extremely thick carbonate reservoir in M Formation of H Oilfield in Iraq，the applicability of conventional permeability logging interpretation models is poor. To solve this problem，this paper proposes a hybrid simulated annealing-genetic algorithm-random forest（SA-GA-RF）algorithm permeability evaluation model with conventional logging data and derived parameters. Depending on the analysis of logging response characteristics，the permeability sensitive curve is determined，and the permeability evaluation model based on the geophysical logging data is constructed by a random forest（RF）algorithm. The simulated annealing-genetic algorithm （SA-GA）is used to optimize the parameters in the RF model，which thus eliminates the influence of key parameters in the RF algorithm on the model accuracy. This method is applied to evaluate the permeability of the study block，and the prediction results are compared with those of RF and the improved back-propagation（BP）neural network. The results show that the SA-GA-RF model for the permeability evaluation of complex carbonate reservoirs can take full advantage of the response characteristics of the conventional logging curves and reflect the trend of logging curves changing with depth. Particularly，it has good applicability in carbonate reservoirs with strong heterogeneity. Compared with the improved BP neural network，the SA-GA-RF model has distinctly enhanced accuracy. The correlation between the core permeability and the prediction result is up to 0.83，which is 0.15 higher than the accuracy of permeability evaluation by RF.