Quantum-behaved particle swarm optimization-based active noise control system with timing varying path

Active noise control systems can effectively suppress the impact of low-frequency noise and they have been applied in many fields. Recently, the evolutionary computation algorithm-based active noise control system has attracted considerable attention. To improve the noise reduction performance of the evolutionary computation algorithm-based active noise control system and solve the problem that the system cannot converge again when the path abruptly changes in steady state, we propose the path abruptly change-quantum-behaved particle swarm optimization algorithm. We apply quantum-behaved particle swarm optimization, a global optimization algorithm, to the active noise control system to improve noise reduction performance. In addition, the scheme of detecting the abrupt path change in steady state and performing re-convergence processing is designed to effectively address the problem that the system cannot regain convergence after a path change in steady state. The simulation study demonstrates that the proposed algorithm can efficiently improve noise reduction performance, accurately detect the path change, and re-converge to new global optimization.

Automated summary

The article introduces an active noise control system based on evolutionary computation algorithm and proposes a path abruptly change-quantum-behaved particle swarm optimization algorithm to address the issue of system unable to re-converge in steady state. Experimental results demonstrate that the proposed algorithm can efficiently enhance noise reduction performance, accurately detect path changes, and re-converge to the new global optimum.