An Improved PSO for Bankruptcy Prediction
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| Title | An Improved PSO for Bankruptcy Prediction |
| Authors | |
| Abstract | Corporate Bankruptcy prediction is a research hot-spot at present. Traditional methods consist of single-variable model and multi-variable model such as neural network (NN). However, the NN can not extract effective rules. Thus, Michigan encoding model was used in this paper to extract effective rules. Furthermore, in order to find the optimal parameters of the Michigan encoding model an improved particle swarm optimization (IPSO) algorithm was proposed which combines the traditional PSO with adaptive population size, adaptive inertia weight and chaotic random number generator. Digital simulation experiments of 800 corporations demonstrate that the Michigan encoding model can effectively extract bankruptcy rules, and IPSO algorithm can find the optimal parameters of the Michigan encoding model with less time and higher successful rate compared with elite genetic algorithm (EGA) and adaptive particle swarm optimization (APSO). |
| Publisher | Advances in Computational Mathematics and its Applications |
| Date | 2012-01-11 |
| Source | 2167-6356 |
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