Determination of Optimal Structure of Capital Using Particles’ Mass Optimization Combination Algorithm

Abstract

The aim of this study was to determine optimal structure of the listed companies on Tehran Stock Exchange using particles’ mass optimization algorithm. Its population included all companies listed on Tehran Stock Exchange from 2004 to 2012 (n= 157). There were selected 100 companies as sample. Particles’ mass combination algorithm was used to analyze data. The results showed that the obtained optimal debt by particles’ mass algorithm is more than or equal to the obtained corresponding values ​​by genetic algorithm in all companies and different industries. Since there is a significant negative relationship between capital structure and rate of return on assets (profitability), lower debt ratio means more profitability. The results indicate that particles mass algorithm has more capable than genetic algorithm to achieve high-quality results. In other words, particles mass algorithm is stronger and more successful than GA to determine optimal capital structure in all companies and different industries.