Affiliations: [a]
School of Economics and Management, Xiamen University of Technology, China
| [b]
Department of Mathematics & Computer Science, Brandon University, Brandon, Canada
| [c]
Research Center for Interneural Computing, China Medical University, Taichung, Taiwan, Republic of China | [d]
College of Computer Science, Inner Mongolia University, Hohhot, China
Abstract: Current accounting methods for small and medium-sized enterprises (SMEs) have long running times and low user satisfaction. Therefore, a method for the selection of accounting models for SMEs based on accounting market big data (AMBD) is proposed in this paper. Firstly, some indicators such as the current ratio, quick ratio, asset-liability ratio, accounts receivable turnover rate, and other indicators taken from the solvency, operating capacity, profitability, and growth capacity of a company are selected to set up an AMBD constraint system. Then, the principal component analysis method is used to achieve the classification of the constraints of the AMBD. Finally, by combining particle swarm optimization with ant colony optimization, the optimal accounting model is obtained through iteration. Experimental results show that the proposed method has high efficiency and user satisfaction, and achieves a high coefficient of rationality. Furthermore, the method incorporates the constraints found in the AMBD, and meets the selection requirements of the SME accounting model.
Keywords: Ant colony optimization, swarm intelligence, accounting, big data, market constraints, accounting models, choice
