Grey Wolf Optimizer Algorithm Based Active Noise Control Without Secondary Path Identification

Intelligent optimization algorithm (IOA) has been widely applied to active noise control (ANC) in recent years. Compared to the conventional filtered-x least mean square (FxLMS) algorithm-based ANC, IOA-based ANC not only eliminates the necessity for secondary path identification but also avoids the problem of converging to local optima. However, the widely used genetic algorithm (GA)-based and particle swarm optimization (PSO)-based ANC exhibit slow convergence rate and poor noise reduction performance. To improve the convergence rate and the noise reduction performance, this paper applies grey wolf optimizer (GWO) algorithm, IOA with enhanced convergence rate and superior global search capability, to the ANC system. Additionally, to address the implementation difficulties and poor real-time adaptability of the conventional offline IOA-based ANC adaptation scheme, a feasible scheme of online IOA-based ANC adaptation with enhanced real-time adaptability is proposed. Simulation results demonstrate that the GWO-based ANC can achieve faster convergence rate and better noise reduction performance.