Blind Source Separation Based on Quantum Slime Mould Algorithm in Impulse Noise

Abstract

In order to resolve engineering problems that the performance of the traditional blind source separation (BSS) methods deteriorates or even becomes invalid when the unknown source signals are interfered by impulse noise with a low signal-to-noise ratio (SNR), a more effective and robust BSS method is proposed. Based on dual-parameter variable tailing (DPVT) transformation function, moving average filtering (MAF), and median filtering (MF), a filtering system that can achieve noise suppression in an impulse noise environment is proposed, noted as MAF-DPVT-MF. A hybrid optimization objective function is designed based on the two independence criteria to achieve more effective and robust BSS. Meanwhile, combining quantum computation theory with slime mould algorithm (SMA), quantum slime mould algorithm (QSMA) is proposed and QSMA is used to solve the hybrid optimization objective function. The proposed method is called BSS based on QSMA (QSMA-BSS). The simulation results show that QSMA-BSS is superior to the traditional methods. Compared with previous BSS methods, QSMA-BSS has a wider applications range, more stable performance, and higher precision.

Slime Mould Algorithm: A Comprehensive Survey of Its Variants and Applications

“…A hybrid optimization has been devised based on the two independence requirements of Blind Source Separation (BSS) to create blind source separation that is both more effective and more resilient. Quantum SMA (QSMA) is suggested by the hybridization of quantum computing theory and SMA, and QSMA is utilized to solve the hybrid optimization algorithm function [ 93 ]. BSS based on QSMA is the name given to the strategy offered as an approach (QSMA-BSS).…”

A Robust DOA Tracking Method Using a Nested Array in Impulse Noise

“…e quantuminspired multiverse algorithm (QMVA) is inspired by quantum computation [26] and theory of cosmology [27]. Assume that Q denotes the number of quantum universes and G denotes the maximum number of iterations.…”

“…erefore, we propose a quantum-inspired multiverse algorithm (QMVA) to avoid this problem, which is inspired by quantum computation [26] and theory of cosmology [27].…”