An Improved Artificial Bee Colony Algorithm for Model-Free Active Noise Control: Algorithm and Implementation

Active noise control (ANC) usually adopts the filtered-x least mean square (FxLMS) algorithm. However, the FxLMS algorithm requires the identification of the secondary path. Problems of time-varying secondary path and imperfect secondary path modeling require model-free ANC algorithms for practical applications. In this article, the artificial bee colony (ABC) algorithm is improved to suitably develop a novel ANC algorithm without secondary path modeling. In addition, the FxLMS algorithm may fall into local minima, while our proposed algorithm features its global optimization ability. In order to have anti-interference ability, in our algorithm, a forgetting factor is introduced into the fitness function. Moreover, the least mean square (LMS) algorithm is integrated into the ABC algorithm to enhance its exploitation ability, further accelerate the convergence rate, and improve the noise reduction performance. Compared with some closely related population-based model-free ANC algorithms, our proposed algorithm has anti-interference ability, faster convergence rate, and better noise reduction performance. Simulations and experiments are conducted to illustrate the effectiveness of the proposed algorithm.

Automated summary

The improved Artificial Bee Colony (ABC) algorithm proposed in this article can develop a novel ANC algorithm without the need for secondary path modeling. This algorithm features global optimization ability and anti-interference ability, with faster convergence rate and better noise reduction performance.