Particle Swarm Optimization

Today, management requires a new approach in the areas of production planning and operations process with a cost management approach. Organizations and industrial units to lead their lives, by recognizing the impact points of the challenges ahead and positive impact, guide and lead them to advance the goals of the organization. Economic dispatching with particle swarm optimization algorithm approach is an approach in the field of industrial units. Dispatching tries to determine the share of production capacity in a way that optimizes the overall performance of the system economically and improve system performance, including: production and process planning, supply and demand balance, cost management, productivity growth, optimal allocation of resources according to the capacity of tanks, formulation of production and operational strategies, the impact on the strategic vision document. In this research, an attempt has been made to perform economic dispatching with the approach of particle swarm optimization algorithm with hypothetical information to measure the feasibility of implementation and its impact on the overall performance of the system and production process and operations.

Financial statements are critical to users, as the increasing fraud cases have left behind irreversible impacts. Hence, this study aims to identify the appropriate financial ratios for fraud risk prediction in the financial statements of companies listed on the Tehran Stock Exchange within the 2014–2021 period. The study is based on data from 180 companies listed on the Tehran Stock Exchange, encompassing a total of 1440 financial statements. To select the most appropriate ratios for fraud risk prediction, all financial ratios were tested by three metaheuristic algorithms, i.e., genetic algorithm, grey wolf optimization, and particle swarm optimization. Metaheuristic and data mining methods were employed for data analysis, and these analyses were conducted using MATLAB R2020a (MATLAB 9.8). According to the research results, the fitness function yielded 0.2708 in particle swarm optimization (PSO). With an accuracy of 72.92% after 19 iterations, PSO was more accurate and converged faster than the other algorithms. It also extracted 11 financial ratios: total debts to total assets, working capital to total assets, stock to current asset, accounts receivables to sales, accounts receivables to total assets, gross income to total assets, net income to gross income, current assets to current debt, cash balance to current debt, retained earnings and loss to equity, and long-term debt to equity. The support vector machine (SVM) classifier was then employed for fraud risk detection at companies through the ratios extracted by the proposed algorithms. The accuracy and precision of financial ratios extracted by PSO and SVM were reported at 80,60% and 71,20%, respectively, which indicates the superiority of the proposed model to other models. Considering that the results obtained from the performance evaluation of financial ratios provided by PSO-SVM demonstrate the capability of this method in predicting the likelihood of fraud in financial statements, it can assist financial statement users. By incorporating these ratios about the performance of the target companies and comparing them with those of other companies, users can make more informed decisions in economic decision-making, investments, credit assessments, and more, ultimately minimizing potential losses and risks.