Oppositional ant colony optimization algorithm and its application to fault monitoring

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.