میز خدمت الکترونیک
دوره ویراستاری

  اخبار و اعلانات

 آمار

تعداد نشریات127
تعداد شماره‌ها7,240
تعداد مقالات77,673
تعداد مشاهده مقاله161,286,311
تعداد دریافت فایل اصل مقاله120,868,229
saeidi, hadi, Hasanzadeh efati, Sara, Elahi Shirvan, Ghasem, Mohammadi, shaban. (1405). Comparative Analysis of Metaheuristic Algorithms and Linear Regression in Predicting the CEO Selection. , 58(1), 28-48. doi: 10.22059/jac.2026.413114.1258
hadi saeidi; Sara Hasanzadeh efati; Ghasem Elahi Shirvan; shaban Mohammadi. "Comparative Analysis of Metaheuristic Algorithms and Linear Regression in Predicting the CEO Selection". , 58, 1, 1405, 28-48. doi: 10.22059/jac.2026.413114.1258
saeidi, hadi, Hasanzadeh efati, Sara, Elahi Shirvan, Ghasem, Mohammadi, shaban. (1405). 'Comparative Analysis of Metaheuristic Algorithms and Linear Regression in Predicting the CEO Selection', , 58(1), pp. 28-48. doi: 10.22059/jac.2026.413114.1258
saeidi, hadi, Hasanzadeh efati, Sara, Elahi Shirvan, Ghasem, Mohammadi, shaban. Comparative Analysis of Metaheuristic Algorithms and Linear Regression in Predicting the CEO Selection. , 1405; 58(1): 28-48. doi: 10.22059/jac.2026.413114.1258

Comparative Analysis of Metaheuristic Algorithms and Linear Regression in Predicting the CEO Selection

Journal of Algorithms and Computation
دوره 58، شماره 1، مهر 2026، صفحه 28-48    اصل مقاله (1.32 M)
نوع مقاله: Research Paper
شناسه دیجیتال (DOI): 10.22059/jac.2026.413114.1258
نویسندگان
hadi saeidi1؛ Sara Hasanzadeh efati1؛ Ghasem Elahi Shirvan2؛ shaban Mohammadi* 3
1Department of Accounting, Shirvan Branch, Islamic Azad University, Shirvan, Iran
2Department of Economy, Shirvan Branch, Islamic Azad University, Shirvan, Iran
3Faculty of mathematical sciences, Shahrood university of technology, Shahrood, Semnan, Iran.
چکیده
The purpose of this study is to perform a comparative analysis of metaheuristic algorithms and linear regression in predicting the selection of CEO of companies listed on the Tehran Stock Exchange for 1050 data consisting of 15 companies for the years 2018 to 2024. The research data was analyzed by combining the particle swarm optimization algorithm and forbidden search algorithms, multilayer perceptron neural network, and gray wolf optimization, in order to predict the selection of CEO. the relationships between variables were examined using the regression method and the results obtained from the metaheuristic algorithms were compared. The results show that the variables of profitability, sales growth, return on assets, and managerial ability are important in all research algorithms in determining the type of CEO. Prediction based on the linear regression model is also possible. The results show the superiority of the neural network model.
کلیدواژه‌ها
CEO Selection؛ Particle Swarm Optimization algorithm؛ Forbidden Search algorithm؛ Neural Network algorithm؛ Grey Wolf Optimization algorithm
آمار
تعداد مشاهده مقاله: 56
تعداد دریافت فایل اصل مقاله: 25





سامانه مدیریت نشریات علمی. قدرت گرفته از سیناوب