The Estimation of Iran's Tax Capacity by Using the Particle Swarm Optimization (PSO) Algorithm and the Genetic Algorithm (GA)

According to the great importance of the tax incomes in supplying the governments’ necessary financial resources, governments and politicians have always taken heed of considering the potential capacity of the taxes. In this paper, the tax capacity function is estimated by utilizing both the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) Algorithm from 1361 to 1389. According to the performance evaluation criteria, the estimated model with the PSO algorithm is selected to assess the tax capacity. The results of the estimated model demonstrate the positive effects of variables of the income per capita, the value added of the industry, mine and services sections to the gross domestic product rate on the tax capacity. Besides, the value added of theagriculture section to gross domestic product rate, inflation rate and unemployment are linked to the tax capacity in a reciprocal relation.