Jaguar Algorithm with Adaptive Territory for Jumping Mechanism applied to Function Optimization Problems

• Main Authors: TSAI, YUNG-CHE, 蔡永哲
• Other Authors: 周耀新
• Format: Others
• Language: en_US
• Published: 2016
• Online Access: http://ndltd.ncl.edu.tw/handle/57421545845830732890

碩士 === 國立暨南國際大學 === 資訊工程學系 === 104 === Meta-heuristic is one of the popular research which is implemented to solve optimization problems in real life. In order to obtain the best solution in limited cost or time, traditional meta-Heuristics are devoted to balancing the capabilities of exploration and exploitation. For the sake of the purpose, traditional methods implement lots of weights (parameters) to balance two capabilities, and implement random variables or numerous population and generation to increase opportunities for finding a better solution. However, the searching mode of traditional methods might have some potential problems. Such as exploration and exploitation in traditional methods might restrict to each other. And implemented parameters shall be adjusted for different problems. Therefore, Jaguar Algorithm is designed in a new concept. We concentrate on exploitation before exploration. At first, proposed method tries its best to find the optimal solution in the arbitrary area. Then it focuses on jumping to better area based on the information of the history. Along the tendency of found areas to find out the place of the global optimum. Proposed method achieves strong capabilities of both exploitation and exploration with these features. Implement adaptive method to adjust approximate parameter according to different problems. The self-analysis and experiment of this research reveal that each capability of Jaguar Algorithm could have various positive effects. And the comparison with traditional meta-heuristics shows outstanding performance of Jaguar Algorithm by benchmark functions.