L’application des algorithmes de colonies de fourmis pour le diagnostic des systèmes dynamiques et complexes

Abstract

System diagnosis is an important field in modern industrial environments for fault detection and improving the reliability of complex systems. In this thesis, we studied the use of the Ant Colony Optimization (ACO) algorithm for system diagnosis. A Python application was developed to simulate artificial ants and search for optimal paths that support fault detection. The obtained results show that ACO is an effective optimization and exploration method despite some limitations related to parameters and execution time.