Conception d'un filtre numérique utilisant les méthodes Méta-heuristiques

Abstract

Digital filters are essential in various applications, including signal processing, communications, and control systems. Traditional filter design methods often face challenges such as getting trapped in local minima and computational inefficiency. Metaheuristic algorithms offer a robust alternative by providing global search capabilities and adaptability to complex optimization problems. In this thesis, we present the design and implementation of digital filters using metaheuristic algorithms, particularly Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and the Improved Cuckoo Search Algorithm (ICSA). The main objective is to explore the effectiveness and efficiency of these algorithms in optimizing the parameters of digital filters to achieve desired signal processing performance.