Bio‐inspired evolutionary computing approach for distributed active noise control problem. Issue 2 (20th May 2020)

• Record Type: Journal Article
• Authors: Kukde, Ruchi; Panda, Ganapati; Manikandan, M. Sabarimalai
• Is Part Of: Cognitive computation and systems. Volume 2:Issue 2(2020)
• Page Start: 57
• Page End: 65
• Publication Date: 2020-05-20
• Subjects: air conditioning -- particle swarm optimisation -- evolutionary computation -- active noise control -- wireless sensor networks -- genetic algorithms -- interference suppression; bio‐inspired evolutionary computing approach -- distributed active noise control problem -- distributed active noise control system -- spatial noise control -- acoustic sensor nodes -- unified strategy -- incremental co‐operative learning -- cat swarm intelligence -- spatial region -- incremental cat swarm optimisation algorithm -- efficient noise -- multiple secondary paths -- developed ICSO‐based DANC scheme -- individual sensor nodes -- global noise mitigation -- distributed network -- tonal air conditioner noise control test scenarios -- broadband air conditioner noise control test scenarios -- practical air conditioner noise control test scenarios -- computational efficiency -- noise cancellation -- different noise cases -- genetic algorithm -- particle swarm optimisation -- DANC counterparts -- noise figure 2.0 dB to 9.0 dB; Cognitive science -- Periodicals; Artificial intelligence -- Periodicals; Neurosciences -- Periodicals; Computer science -- Periodicals; Neurosciences; Computer science; Cognitive science; Artificial intelligence; Periodicals; Electronic journals; 006.3
• DOI: 10.1049/ccs.2019.0030
• Languages: English
• ISSNs: 2517-7567

Abstract

Abstract : In this study, a distributed active noise control (DANC) system for spatial noise control in a network of acoustic sensor nodes based on the behavioural traits of felines is presented. An unified strategy based on incremental co‐operative learning and cat swarm intelligence is proposed for noise mitigation in spatial region. The hybrid nature of the proposed incremental cat swarm optimisation (ICSO) algorithm provides efficient noise control without prior estimation of multiple secondary paths. In the developed ICSO‐based DANC scheme, the individual sensor nodes communicate the intermediate solutions using incremental mode of cooperation to attain overall global noise mitigation over the distributed network. The performance of the proposed ICSO based DANC scheme is validated for tonal, broadband and practical air conditioner noise control test scenarios. Evaluation results show that the proposed system achieves faster convergence with computational efficiency of over 36% and ∼2–9 dB improvement in noise cancellation for different noise cases and acoustic environments over genetic algorithm and particle swarm optimisation based DANC counterparts.