جستجوی مقالات مرتبط با کلیدواژه « particle swarm optimization algorithm » در نشریات گروه « صنایع »

Banks' inability to credit assessment and financial evaluation of customers and forecasting accurately the credit risk of borrowers has devastating effects on the global financial system and economic activity and have been the main causes of global financial crises in recent years.The purpose of this paper is to compile a credit forecasting model for legal customers of private banks by using meta-heuristic algorithms in the branches of Pasargad Bank in the northwest of Iran.

This research is base on the purpose of developmental research and based on the method of performing descriptive work. The statistical population of this study is in two sections of banking experts and legal customers of Pasargad Bank in the northwest of the Iran. The statistical sample size for the first community of 58 banking experts including managers, credit officials and heads of branches in with credit work experience in private banks and for the second community, 427 legal clients were selected based on targeted sampling. In order to collect data in this research, a questionnaire and documents of Pasargad Bank have been used. The validity of the questionnaire was investigated as content validity and based on the indicators of content validity ratio and content validity index. The reliability of the questionnaire was assessed using Cronbach's alpha coefficient. In order to analyze the research data, t-test, confirmatory factor analysis, multilayer neural network, genetically trained neural network, trained neural network with particle swarm optimization and trained neural network with differential evolution will be used.

 The research findings show that all four models are able to predict the credit predictions of the legal customers of private banks and the best way to predict the credit predictions of the legal customers of private banks is the neural network trained with differential evolution algorithm with the least amount of error compared to the other three methods.Originality/Value: In this research by using meta-heuristic algorithms, a new credit forecasting model produce for legal customers of private banks with the least amount of error.

The middle repository is a relatively new logistics technique that has been used successfully in a large number of retail chains. In this system, repositories instead of being points for storing goods, are coordinators of the stock. This paper by modeling two objective functions simultaneously has simulated and optimized the problem of routing vehicle with middle repository and possibility of slight sending of products to customers. In this way, vehicles after leaving repository through some routes collect goods from suppliers and return to repository and after the separation and integration of products within repository, distribute items among customers with respect to slight sending in routes or send items directly from factory to customers.
Because of the inherent complexity of integer hybrid models and their NP-hardness, they cannot be solved by exact methods. In this paper, optimization of the mass of particles and the Tabu search as two meta-heuristic algorithms and a heuristic algorithm for searching neighborhoods are used and the results of the five data sets with different sizes are analyzed. The findings show that the algorithm of optimization of the mass of particles despite being time-consuming finds acceptable and near-optimal solutions and heuristic algorithm of searching neighborhoods despite good results is in the second rank. However, in terms of time, it takes minimum time to find near-optimal solution.