Developing an enhanced ant colony search algorithm for continuous optimization problems

• Main Authors: Yi-Chen Chen, 陳以承
• Other Authors: Tzu-Yi Yu
• Format: Others
• Language: zh-TW
• Published: 2013
• Online Access: http://ndltd.ncl.edu.tw/handle/54872652362211160719

碩士 === 國立暨南國際大學 === 資訊管理學系 === 101 === Optimization applications can be found easily in our daily life. Optimization is a mathematical practice that focuses on the finding the functional minima (or maxima) subject to functional constraints. However, most real life applications are high orders and nonlinear after converting to mathematical formula. Traditional and theoretical mathematical approaches fail to the solutions to these problems. On the other hand, heuristic algorithm has gained more attentions as it can be used to fit this requirement and solve the complex optimization problems. Among the heuristic algorithms, the Ant Colony Optimization (ACO) is one of the popular methods use for optimization problems with discrete domain. In this study, we embed the evolutionary mechanism and develop an enhanced method to make ACO suitable for continuous optimization problem. Various benchmark problems have been tested to verify the robustness and accuracy. The results show this proposed approach can be applied to large dimensional optimization problem with continuous domain.