particle cumulative motion optimization algorithm

The purpose of this study is to optimize the Bayesian profit management model with tunneling phenomenon and cumulative particle motion optimization algorithm. The statistical population of the study included companies listed in the Tehran Stock Exchange and the number of companies under study, including 196 companies listed during the years 2015 to 2020. The research method is descriptive-correlational and in terms of causal-correlational variables and in terms of purpose and event, it is post-event. In order to analyze the data, regression and artificial neural network and cumulative particle motion optimization algorithm were used. The results of the model analysis showed that all financial ratios had a significant effect on the earnings management prediction of insight and the greatest impact on the prediction of earnings management was on the INE tunneling phenomenon and the least on financial leverage. The results of the estimation of the designed neural networks show that the use of cumulative particle optimization algorithm to predict the Profit management for companies listed in Tehran Stock Exchange is acceptable.