文献类型：会议论文

英文题名：Oppositional Ant Colony Optimization Algorithm and Its Application to Fault Monitoring

作者：Ma Haiping[1];Ruan Xieyong[1];Jin Baogen[1]

机构：[1]Shaoxing Univ, Dept Phys & Elect Engn, Shaoxing 312000, Peoples R China

会议论文集：29th Chinese Control Conference

会议日期：JUL 29-31, 2010

会议地点：Beijing, PEOPLES R CHINA

语种：英文

外文关键词：Evolutionary Algorithms; Ant Colony Optimization; Opposition-Based Learning; Fault Monitoring

外文摘要：In order to improve the real time of aircraft engine fault monitoring, it applies ant colony optimization (ACO) to select feature parameters of fault monitoring. To tackle the slow nature of ACO, an oppositional ant colony optimization (OACO) is presented in this paper. Utilizing the acceleration performance of opposition-based learning (OBL), it employs OBL for pheromone updating to accelerate the evolutionary process, improve the searching capability, and shorten the computing time. Also it has some merit including simpleness and easy implement. Through benchmark functions and monitoring parameter selection problem, it demonstrates that the proposed algorithm is effective and superior.