Model-free active noise control using improved grey wolf optimizer algorithm with conditional reinitialization strategy

Zhehua Duan

doi:10.1016/j.dsp.2025.105730

Model-free active noise control using improved grey wolf optimizer algorithm with conditional reinitialization strategy

2025-11-12

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DOI：10.1016/j.dsp.2025.105730

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Active noise control (ANC) using population-based metaheuristic algorithms (MAs) has recently attracted considerable interest because it does not require secondary path modeling and is less prone to falling into local optima. The Grey Wolf Optimizer (GWO), as a population-based MA, has attracted significant research attention due to its capability to balance global exploration and local exploitation. This paper proposes an improved GWO algorithm and applies it to the ANC system to enhance both the convergence speed and noise reduction performance. Moreover, to address the latency issue inherent in traditional generation-by-generation detection-based conditional reinitialization strategy, this paper proposes a novel sample-by-sample detection-based conditional reinitialization strategy to enhance the system's responsiveness to secondary path changes. Simulation results demonstrate the effectiveness of the proposed algorithm.

Model-free active noise control using improved grey wolf optimizer algorithm with conditional reinitialization strategy

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