Dynamic Nonlinear Active Noise Control: A Multi-objective Evolutionary Computing Approach

Evolutionary-computing-algorithm-based nonlinear active noise control (ANC) removes the requirement of secondary path modeling, which is essential for proper functioning of a conventional gradient-descent-approach based ANC system. However, the noise mitigation capability of such algorithms is largely dependent on the proper selection of the agent count as well as on the number of sound samples processed by an agent in a given iteration. In order to alleviate this dependency, we propose a dynamic nonlinear ANC (DNANC) system, which adapts its parameters in accordance with the acoustic scenario under consideration. The nonlinear ANC (NANC) problem has been formulated as a multi-objective optimization problem in this chapter. We have used the non-domination sorting genetic algorithm II (NSGA-II) for solving the optimization task. The conflicting objectives employed in this chapter are the ensemble mean-square error and the computation time. The proposed DNANC system has been shown to adapt itself to several ANC scenarios in a dynamic manner, wherein, the controller structure has been optimized for the situation considered.