Differential evolution is one of the most efficient optimization algorithms for solving complication problems including single objective multiobjective and many-objective optimization. It is a stochastic population-based search approach for optimization over the continuous space. The main advantages of differential evolution are simplicity robustness and high speed of convergence. The Advanced Fuzzy-based Multiobjective Differential Evolution (AFMDE) that exploits three performance metrics specifically hypervolume spacing and maximum spread to measure the state of the evolution process. The fuzzy inference rules are applied to these metrics in order to adaptively adjust the associated control parameters of the chosen mutation strategy used in AFMDE. The optimization algorithm will stop the evolution process if the number of iterations reaches the stopping criteria which usually is the maximum number of iterations. Then the optimization algorithm delivers the optimal solution founded. However sometimes if the maximum number of iterations is not appropriately defined the found solutions may not be the optimal ones. In case of the optimization algorithm has found the optimal solutions but it must continue the evolution process because the stopping criteria are not met. This can cause unnecessary using of high computational resources and time-consuming. Therefore this research study proposed the stopping criteria based on performance metrics feedback for AFMDE. The efficiency of the proposed criteria combined with AFMDE is evaluated on the well-known ZDT benchmark test suites.

The objective of this study was to construct the black pepper prices forecasting model by 4 statistical methods: Box-Jenkins method Holt’s exponential smoothing method Brown’s exponential smoothing method and damped trend exponential smoothing method. Time series of monthly black pepper prices which were gathered from the website of the Office of Agricultural Economics during January 2005 to December 2018 of 168 observations were divided into 2 datasets. The first dataset had 156 observations from January 2005 to December 2017 for constructing the forecasting models. The second dataset had 12 observations from January to December 2018 for comparing the accuracy of the forecasts via the criteria of the lowest mean absolute percentage error (MAPE) and root mean squared error (RMSE). Research findings indicated that for all forecasting methods that had been studied the most accurate method was damped trend exponential smoothing method (MAPE = 6.5773 RMSE = 10.3775)

The purposes of this research were 1) to develop the model of group process Learning. 2) to compare the learning achievement between before and after learning from the group process Learning 3) to study the satisfaction of students of group process Learning in Thai for Presentation Course of the Students at Rajamangala University of Technology Pha Nakhon. This is an experimental research. The sample is 30 students in 1st year undergraduate in tourism major by purposive sampling who studied Thai for Presentation Course in the first semester (2019) at faculty of Liberal Arts Rajamangala University of Technology Pha Nakhon. The research instruments used in the experiment were 4 sets of group process learning management plan 1 set of achievement test used before and after learning and 1 set of student satisfaction questionnaire for group process Learning. The results were as follows : 1. The development of The group process learning using 4 sets which included small group process learning brainstorming group process learning discussion group process learning and the presenting group process learning was developed and applied in teaching and learning Thai for presentation course After the application the program was evaluated and it was found that the Index of Item Objective Congruence (IOC) of the evaluation of the quality of the group process management learning reached at 0.67. The Index of Item Objective Congruence (IOC) of achievement test reached at 1.00. And the Index of Item Objective Congruence (IOC) of the satisfaction reached at 1.00.

The objectives of this research are to estimate the morbidity rates of Hypertension and Ischemic heart disease in each province of Thailand to investigate factors influencing on the morbidity rates and to construct the disease maps of those two diseases. The generalized linear mixed model (GLMM) in which the responses have a Poisson distribution and the spatial effects are a multivariate conditional autoregressive model (MCAR) are adopted. The provincial secondary data sets consisting of a number of Hypertension and Ischemic heart disease patients an average income per head and a student proportion were collected. The results show the average income per head (Relative Risk (RR) = 1.0002) and student proportion (RR = 1.0186) influence the morbidity rates of Hypertension and Ischemic heart disease. Hypertension and Ischemic maps are easy for readers to see which areas are at high risk. They are a useful tool for planning and controlling the Hypertension and Ischemic heart disease.

Resource leveling schedule is important for construction planning process very much because quantity of resource such as labors is limited. The optimal solution to allocate enough resource for every activity is main principle for project successful. But in ordinary they always observe that have different quantity of resource every time periods is called of “Resource fluctuation”. So planners have to adjust first schedule is called of “Resource Leveling”. This method necessary for decrease resource fluctuations or make uniformly resource demand level . However normal method (heuristic method) to solve this problem is difficult and use much time. So in present there is new developed method by create model in spreadsheet computer programs such as Microsoft Excel. This new method can solve very complex resource leveling problem well..So in this research if we add vary relationship options such as SS (Start-To-Start) FF (Finish-To- Finish) or NO (No Relation) for activity in model objective is increasing optimal solution flexibly and output solution is better than El Rayes & Jun ‘s Model. We can discover for optimal solution from this new model that received uniformly resource demand level of project schedule in the end