Application of the genetic algorithm to real-time active noise control

Tang, Wallace K. S.; Man, K.F.; Kwong, S.; Chan, Ching Yuen; Chu, C. Y.

doi:10.1007/bf00337683

Cited by 6 publications

(1 citation statement)

References 9 publications

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“…The local search algorithms are easy to implement but suffer from premature convergence, i.e., local minima issues. To overcome these issues, different global search based soft computing techniques are proposed such as, genetic algorithms (GAs) [45,46], particle swarm optimization (PSO) [47][48][49], backtracking search heuristics [50], fireworks algorithm [51], and artificial neural networks [52]. The optimization strength of FPA looks promising to be exploited for ANC problems as an alternate, accurate, reliable, and robust computing paradigm.…”

Section: Introductionmentioning

confidence: 99%

Flower Pollination Heuristics for Nonlinear Active Noise Control Systems

Khan¹,

He²,

Raja³

et al. 2021

Computers, Materials &Amp; Continua

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In this paper, a novel design of the flower pollination algorithm is presented for model identification problems in nonlinear active noise control systems. The recently introduced flower pollination based heuristics is implemented to minimize the mean squared error based merit/cost function representing the scenarios of active noise control system with linear/nonlinear and primary/secondary paths based on the sinusoidal signal, random and complex random signals as noise interferences. The flower pollination heuristics based active noise controllers are formulated through exploitation of nonlinear filtering with Volterra series. The comparative study on statistical observations in terms of accuracy, convergence and complexity measures demonstrates that the proposed meta-heuristic of flower pollination algorithm is reliable, accurate, stable as well as robust for active noise control system. The accuracy of the proposed nature inspired computing of flower pollination is in good agreement with the state of the art counterpart solvers based on variants of genetic algorithms, particle swarm optimization, backtracking search optimization algorithm, fireworks optimization algorithm along with their memetic combination with local search methodologies. Moreover, the central tendency and variation based statistical indices further validate the consistency and reliability of the proposed scheme mimic the mathematical model for the process of flower pollination systems.

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