particle swarm algorithm

The goal of the study is to estimate an artificial neural network (ANN) model for bankruptcy prediction and optimize processes using the Particle Swarm (PSO) and Genetic (GA) algorithms. 21 variables that were related to the likelihood of bankruptcy were chosen for the study. Neural networks (NNs) choose the optimal network with the least error in training and evaluating patterns in the second phase. The neural network's weights and biases were optimized in the final stage by combining GA and PSO with the neural network. The results showed that the ability to explain the initial pattern has risen using GA and PSO. The evaluation of ANN performance demonstrates the superiority of the models over linear regression. Finally, four variables—current ratio, sales to current assets ratio, economic value added, and gross profit margin ratio—that may reliably predict bankruptcy were found using the ANNs-PSO and ANNs-GA hybrid approach. The evidence reveals the effectiveness of the metaheuristic algorithms compared to linear ones in predicting bankruptcy. This further highlights the new breed of computational tools available to techno-savvy financial analysts and investors.

The aim of this study was to evaluate the integrated risk of the banking system through the meta-heuristic algorithms of gray wolf, genetics and particle swarming. This research is applied research in terms of purpose and correlational in nature and method. Data collection has been done through library studies, articles and sites in deductive form and data collection to refute and confirm hypotheses inductively. The statistical population of this research is the banking system and the sample includes banks listed on the Tehran Stock Exchange during the fiscal years 1392 to 1397. In order to collect the required data, the financial database of the Ministry of Economic Affairs and Finance, codal website, etc. have been used. After extracting the information, and adjusting them in the form of an integrated risk model, the objective function and constraints are entered in MATLAB software and the variables of risk and return profit and loss on assets and Debts were obtained using particle swarm algorithms, genetics, and gray wolves, and we compared their results using SPSS 16 software. After that, first the descriptive statistics were analyzed and then inferential statistics were performed. after reviewing the results of comparing the evaluation indicators of algorithms, it was determined that the gray wolf algorithm is efficient. Provides better goal function optimization. Also, by examining the research hypotheses, it was found that particle swarm algorithms and genetics have the same efficiency for assessing the integrated risk of the banking system. Provides better problem solving.