A novel sensor array and classifier optimization method of electronic nose based on enhanced quantum-behaved particle swarm optimization

Abstract

Purpose -The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array's optimization and parameters' setting of classifier have a strong impact on the classification accuracy. Design/methodology/approach -An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification. Findings -The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier's parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection. Research limitations/implications -To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose. Practical implications -In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring. Originality/value -The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.

Bio-inspired weighed quantum particle swarm optimization and smooth support vector machine ensembles for identification of abnormalities in medical data

“…adaptive network-based fuzzy inference system (ANFIS) is developed to forecast the market price based on the wavelet of time series by using its membership functions to tune the quantum particle swarm optimization (QPSO) [17]. The novel time warping (DTW)-wavelet transform (WT) method is used to extract the patterns automatically [18]. PSO provides better results in handling the arbitrary cost of nonlinear functions than the genetic algorithm (GA) [19].…”

Research on an Improved Quantum Particle Swarm Optimization and its Application

“…Zhang and Sun [29] proposed a modified quantum-behaved particle swarm optimization algorithm based on learning from excellent individuals. Jia et al [30] proposed a quantum-behaved particle swarm optimization. Wang et al [31] proposed an improved the quantum particle swarm optimization (QPSO) algorithm using quantum HΕgate and quantum rotation gate in changing quantum probability amplitude, changing the mutation operator with Quantum Hadamard Gate and modifying constant Inertia Weight to random inertia weight.…”

An Enhanced Quantum-Behaved Particle Swarm Optimization Based on a Novel Computing Way of Local Attractor

“…PSO, SPSO, SQPSO and EQPSO all initialize their particle swarm by random numbers uniformly distributed, and other sets of these four methods are as introduced in Section 2. The set of M1, M2 and M3 was the same as [6,7,11]. The dimension of the all seven test functions was 30, and each program was repeated 10 times.…”

“…Besides EQPSO, PSO, SPSO, and SQPSO, the enhanced QPSO proposed in paper [6,7,11] (for simplicity, these three methods are denoted as M1, M2 and M3) are also used to optimize the seven test functions.…”

“…The QPSO algorithm has been greatly improved in the aspects of search ability, convergence speed, OPEN ACCESS solution accuracy and solving robustness, and it can also overcome the shortcoming that the standard PSO (SPSO) cannot guarantee the global convergence [1,2]. For this reason, QPSO has been widely used in bio-medicine, antenna design, combinatorial optimization, signal processing, neural networks and other fields [3][4][5][6][7][8][9][10][11].…”