Wen-Tsao Pan

A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example

The treatment of an optimization problem is a problem that is commonly researched and discussed by scholars from all kinds of fields. If the problem cannot be optimized in dealing with things, usually lots of human power and capital will be wasted, and in the worst case, it could lead to failure and wasted efforts. Therefore, in this article, a much simpler and more robust optimization algorithm compared with the complicated optimization method proposed by past scholars is proposed; the Fruit Fly Optimization Algorithm. In this article, throughout the process of finding the maximal value and minimal value of a function, the function of this algorithm is tested repeatedly, in the mean time, the population size and characteristic is also investigated. Moreover, the financial distress data of Taiwans enterprise is further collected, and the fruit fly algorithm optimized General Regression Neural Network, General Regression Neural Network and Multiple Regression are adopted to construct a financial distress model. It is found in this article that the RMSE value of the Fruit Fly Optimization Algorithm optimized General Regression Neural Network model has a very good convergence, and the model also has a very good classification and prediction capability.

Using multi-stage data mining technique to build forecast model for Taiwan stocks

Taiwan stock market trend is fast changing. It is affected by not only the individual investors and the three major institutional investors, but also impacted by domestic political and economic situations. Therefore, to precisely grasp the stock market movement, one must build a perfect stock forecast model. In this article, we used a multi-stage optimized stock forecast model to grasp the changing trend of the stock market. First, data of 2 stocks, TSMC and UMC were collected, and then inputted the test data into the genetic programing and built a model to find out the arithmetic expressions. Artificial Fish Swarm Algorithm is used to dynamically adjust the variable factors and constant factors in the arithmetic expressions. Next, we took the error term (ε) in arithmetic expressions to Gray Model Neural Network to make the forecast. Finally, we used the Artificial Fish Swarm Algorithm to dynamically adjust the parameters of the Gray Model Neural Network to enhance the precision of the stock forecast model as a whole. The result showed that the forecast capability of each stage after the optimization process is better than that of its previous stage, and the mixed stock forecast model (GP–AFSA+GMNN–AFSA) in stage 4 greatly enhanced the precision of the forecast.

A novel multi-scale cooperative mutation Fruit Fly Optimization Algorithm

Abstract The Fruit Fly Optimization Algorithm (FOA) is a widely used intelligent evolutionary algorithm with a simple structure that requires only simple parameters. However, its limited search space and the swarm diversity weaken its global search ability. To tackle this limitation, this paper proposes a novel Multi-Scale cooperative mutation Fruit Fly Optimization Algorithm (MSFOA). First, we analyze the convergence of FOA theoretically and demonstrate that its convergence depends on the initial location of the swarm. Second, a Multi-Scale Cooperative Mutation (MSCM) mechanism is introduced that tackles the limitation of local optimum. Finally, the effectiveness of MSFOA is evaluated based on 29 benchmark functions. The experimental results show that MSFOA significantly outperforms the improved versions of FOA presented in recent literature, including IFFO, CFOA, and CMFOA, on most benchmark functions.

The use of genetic programming for the construction of a financial management model in an enterprise

The fast development in China’s economy has caused the rapid expansion of the domestic market. Since many economists do not have optimistic views regarding the bubble economy of China, it is necessary for Taiwanese businessmen to understand in-depth the business operational performance and financial situation of enterprises in China, so as to reduce the risk of a potential investment. In this article, data from the China Economic Research Database (CCER), the financial database of financial corporations are collected for analysis to investigate the business operation and management performance and financial characteristic of enterprises in China. In this article, grey relational analysis is applied first in order to investigate the business operational performance of 600 enterprises in China. Afterwards, a more recent clustering technique is used to divide, based on financial characteristic, enterprises in China into two groups. Finally, three models, namely genetic programming, Back-Propagation Neural Network and Logistic Regression are adopted to construct an Enterprise Operational Performance model and an Enterprise Finance Characteristic model, respectively. Based on the results found, it can be concluded that genetic programming yielded the best classification and forecast performance, compared to the other three techniques.

Mixed modified fruit fly optimization algorithm with general regression neural network to build oil and gold prices forecasting model

Purpose – When facing a clouded global economy, many countries would increase their gold reserves. On the other hand, oil supply and demand depends on the political and economic situations of oil producing countries and their production technologies. Both oil and gold reserve play important roles in the economic development of a country. The paper aims to discuss this issue. Design/methodology/approach – This paper uses the historical data of oil and gold prices as research data, and uses the historical price tendency charts of oil and gold, as well as cluster analysis, to discuss the correlation between the historical data of oil and gold prices. By referring to the technical index equation of stocks, the technical indices of oil and gold prices are calculated as the independent variable and the closing price as the dependent variable of the forecasting model. Findings – The findings indicate that there is no obvious correlation between the price tendencies of oil and gold. According to five evaluating i...

Using data mining for service satisfaction performance analysis for mainland tourists in Taiwan

Since Taiwan and mainland China signed the Economic Cooperation Framework Agreement (ECFA) across the Taiwan Strait, the number of mainland tourists visiting Taiwan has grown significantly. To cope with the needs of mainland tourists, Taiwan must reinforce the software and hardware facilities and service quality of its entire tourism industry. This will attract more tourists to Taiwan and create more opportunities for the Taiwanese tourism industry. In this article, tourists visiting Taiwan are asked to complete a questionnaire survey; we then use the satisfaction information gathered to perform grey relational analysis so as to understand the best and worst scoring questions related to satisfaction. From the analysis results, it can be seen that in the assessment of satisfaction question performance, Taiwanese cuisine scores the highest, the cleanliness of Taiwan’s streets scores the lowest, and of people interviewed between 30 and 40 years old, more rated satisfaction performance and characteristics negatively.

Using data mining technique to perform the performance assessment of lean service

Lean production and service means to improve the production and service management within an enterprise. Through several management techniques, the waste, the redundant things, and the expenses without added values can be cleared, and the production and service within an enterprise will become smoother, that is, the competitiveness of an enterprise will be enhanced. In this study, the data collected from experiment carried out in Toyota lean service simulation laboratory of China University of Technology are used for analysis. Data mining technique is used to investigate whether the result of lean production and service taken by enterprises can enhance the entire performance of production and service. In this study, Grey relational analysis is performed first and is used to judge whether the data collected in the experiment using lean production and service can enhance performance; then, clustering method is used to classify experimental data into two clusters based on service attitude and dish-serving efficiency; finally, three data mining techniques of Genetic Programming (GP), Back-propagation Artificial Neural Network and logistic regression are used to set up, respectively, lean service performance model and Employee Characteristic Analysis model. From the analysis result, it is shown that the result of lean production and service can indeed enhance the performance of entire production and service; and among the three data mining techniques, GP model has the best classification and forecast capability.

Forecasting classification of operating performance of enterprises by ZSCORE combining ANFIS and genetic algorithm

Classification of operating performance of the enterprises is not only a hot issue emphasized by the management, but it is an important reference for investors too in their decision-making. Generally speaking, when predicting or analyzing business performance classification, most researchers adopt corporate financial early warning or credit-rating models, which pretty much use previous data and facts. Therefore, this paper brings about an alternative method to discriminate between excellent and poor business management, so as to take preventive measures prior to business crisis or bankruptcy. We collected the financial reports and financial ratios from the listed firms in mainland China and Taiwan as our samples to build up four kinds of forecasting models for business performance. The empirical results show that the hybrid model provides better classification forecasting capability than the other models, while the ANFIS model adjusted by genetic algorithm could effectively enhance the classification forecasting capability.

Combining fuzzy sammon mapping and fuzzy clustering approach to perform clustering effect analysis: Take the banking service satisfaction as an example

Hard clustering and fuzzy clustering analysis is the basis of the construction of many classifications and systems with its main focus on planning and dividing data into many subsets according to certain rules. Due to the practical function of clustering analysis, many researchers thus proposed different clustering algorithms to be used by researchers around the world. In this article, Fuzzy sammon Mapping method is implemented to perform clustering effect and classification capability analysis on these frequently used clustering algorithms. From the result of test data of an investigation of banking service satisfaction, GK Cluster algorithm was found to have very good clustering effect; however, as for classification capability, hard clustering analysis method has proved to be the better approach amongst the two.

Study on the performance evaluation of online teaching using the quantile regression analysis and artificial neural network

This paper is totally different than the research design and research method in the related literature for investigating how information technology-based reading and learning process of network distance teaching affects the assessment result in the past, that is, innovative research architecture and process is adopted. Here, quantile regression analysis is applied to the investigation of how the time and frequency of log-in curriculum, browsing teaching material, and curriculum discussion in learning process record affects the final-term assessment result of multimedia design digital teaching material subject. In depth research is done under such research architecture, and it is hoped that how each independent variable affects the final-term assessment result under different quantile can be investigated. In addition, this paper has applied new artificial neural network technology to set up expert system for teacher’s assessment result in distance teaching so as to reduce teacher’s teaching pressure, moreover, the result can be used as reference by general researchers and scientific education researchers. The research result shows that the use of quantile regression to analyze the influence of different variable on the teacher’s final-term assessment result of distance teaching is a feasible way; FOAGRNN model, as compared to other five models, has better forecasting capability.