Jaguar Algorithm with Dynamic Updating Vectors Strategy to Solve Function Optimization Problems

• Main Authors: YEH, YI-TING, 葉奕廷
• Other Authors: CHOU, YAO-HSIN
• Format: Others
• Language: en_US
• Published: 2017
• Online Access: http://ndltd.ncl.edu.tw/handle/81432083619882505614

碩士 === 國立暨南國際大學 === 資訊工程學系 === 105 === Meta-heuristic is one of the popular research which is implemented to solve optimization problems in real life. Among these optimization problems, the problems with dependency are the most difficult to solve. In order to obtain the optimal solution in limited cost or time, traditional meta-heuristics are devoted to balancing the capabilities of exploration and exploitation. This study uses Jaguar Algorithm (JA), which is designed in a new concept. JA uses bisection so that it can rapidly find the optimal solution in function without dependency. The proposed method uses dynamic updating vectors strategy to solve dependency problems and improves the hunting of jaguar algorithm to make it more efficiently. And the comparisons with previous JA and traditional meta-heuristics show outstanding performance of Jaguar Algorithm in benchmark functions.