Comparative Study of GA and PSO Algorithms in EMI Filter Optimization

Electromagnetic Interference (EMI) filters are used as critical components in a wide range of area to ensure compliance with electromagnetic compatibility (EMC) regulations. EMI filters involve trade-offs between electrical performance and physical constraints. This paper discusses a multi-objective optimization-based approach for designing EMI filters using two widely known metaheuristic algorithms: Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). These methods were comparatively investigated for the design of EMI filters. Both algorithms aimed to reach desired cut-off frequencies, reduce noise levels and minimize the total volume by selecting the filter elements in the most appropriate way. The convergence speeds and solution efficiencies of the algorithms were evaluated in the simulations performed in the MATLAB environment.