A novel application of integrated grasshopper optimization heuristics for attenuation of noise interferences

Novel application of integrated swarming intelligence computing paradigm is exploited for reliable treatment of nonlinear active noise control (ANC) systems using global search capacity of grasshopper optimization algorithm (GOA) combined with local search efficacy of sequential quadratic programming (SQP), i.e., GOA-SQP. The designed optimization mechanism GOA-SQP is applied to minimize the cost function of ANC controller incorporating the nonlinear Volterra filtering having linear/nonlinear primary/secondary paths in case of different noise interferences of sinusoidal, random, and complex random type signals. The comparison of the results through statistical observations in terms of accuracy, convergence and complexity indices reveals that the GOA-SQP based ANC controllers are operative, resilient and viable.(c) 2021 The Authors. Production and hosting by Elsevier B.V. on behalf of Ain Shams University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Automated summary

This study proposes a novel approach, GOA-SQP, which utilizes integrated swarming intelligence computing paradigm for reliable treatment of nonlinear active noise control systems. The experimental results demonstrate that the ANC controllers based on GOA-SQP exhibit operational capability, resilience, and feasibility.