Quantum Particle Swarm Optimization Research Articles

A Classification and Prediction Hybrid Model Construction with the IQPSO-SVM Algorithm for Atrial Fibrillation Arrhythmia

Atrial fibrillation (AF) is the most common cardiovascular disease (CVD), and most existing algorithms are usually designed for the diagnosis (i.e., feature classification) or prediction of AF. Artificial intelligence (AI) algorithms integrate the diagnosis of AF electrocardiogram (ECG) and predict the possibility that AF will occur in the future. In this paper, we utilized the MIT-BIH AF Database (AFDB), which is composed of data from normal people and patients with AF and onset characteristics, and the AFPDB database (i.e., PAF Prediction Challenge Database), which consists of data from patients with Paroxysmal AF (PAF; the records contain the ECG preceding an episode of PAF), and subjects who do not have documented AF. We extracted the respective characteristics of the databases and used them in modeling diagnosis and prediction. In the aspect of model construction, we regarded diagnosis and prediction as two classification problems, adopted the traditional support vector machine (SVM) algorithm, and combined them. The improved quantum particle swarm optimization support vector machine (IQPSO-SVM) algorithm was used to speed the training time. During the verification process, the clinical FZU-FPH database created by Fuzhou University and Fujian Provincial Hospital was used for hybrid model testing. The data were obtained from the Holter monitor of the hospital and encrypted. We proposed an algorithm for transforming the PDF ECG waveform images of hospital examination reports into digital data. For the diagnosis model and prediction model trained using the training set of the AFDB and AFPDB databases, the sensitivity, specificity, and accuracy measures were 99.2% and 99.2%, 99.2% and 93.3%, and 91.7% and 92.5% for the test set of the AFDB and AFPDB databases, respectively. Moreover, the sensitivity, specificity, and accuracy were 94.2%, 79.7%, and 87.0%, respectively, when tested using the FZU-FPH database with 138 samples of the ECG composed of two labels. The composite classification and prediction model using a new water-fall ensemble method had a total accuracy of approximately 91% for the test set of the FZU-FPH database with 80 samples with 120 segments of ECG with three labels.

Grain Temperature-Dependent Thermal Properties Estimation Using FI-QPSO Algorithm

Abstract A hybrid fuzzy inference-quantum particle swarm optimization (FI-QPSO) algorithm is developed to estimate the temperature-dependent thermal properties of the grain. The fuzzy inference scheme is established to determine the contraction-expansion coefficient according to the aggregation degree of particles. The heat transfer process in the grain bulk is solved using the finite element method, and the estimation task is formulated as an inverse problem. Numerical experiments are performed to study the effects of the surface heat flux, measurement errors, and the individual space on the estimation results. Comparison with the quantum particle swarm optimization (QPSO) algorithm and conjugate gradient method (CGM) is also conducted, and it shows the validity of the estimation method established in this paper.

A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems

Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.

Theoretical investigation on inverse identification of spectral properties of paraffin phase change materials based on multi-thickness model

Paraffin as an organic phase change material with high latent heat and stable physical and chemical properties, is widely used as building filling materials to absorb and store solar energy. And the spectral properties of paraffin is crucial to study the phase change characteristics of paraffin materials in absorbing solar energy. Therefore, this work studies theoretically the inverse identification of spectral properties of paraffin phase change materials. A multi-thickness model is developed to determine the optical constants (spectral refractive index and absorption index) of paraffin materials in solid and liquid state based on transmittance data. This model overcomes ill-posed and multiple solution of inverse problems. An improved quantum particle swarm optimization algorithm is adopted to determine spectral refractive index and absorption index of paraffin materials with high accuracy. A serious of numerical experiments are implement to study retrieval problems thoroughly and results show that the inverse strategy we develop is a promising technique to obtain spectral properties of paraffin materials.

Research on 3D Laser Scanning Monitoring Method for Mining Subsidence Based on the Auxiliary for Probability Integral Method

When 3D laser scanning technology is used to monitor the surface deformation of mining subsidence in mining area, the surface of the working face is covered with a large number of vegetation, and the surface water accumulates above the working face, which makes the point cloud data obtained by the 3D laser scanning difficult to denoise, or even missing. At this time, the conventional 3D laser scanning technology can not obtain the surface deformation field of mining subsidence. Aiming at the above problems, the 3D laser scanning monitoring method for mining subsidence based on the auxiliary for PIM proposed (3DLS-PIM). Firstly, this paper introduces the PIM prediction model. Secondly, the mining subsidence observation equation based on 3DLS-PIM is constructed, and then the prediction parameters of PIM are solved based on quantum particle swarm optimization (QPSO). Finally, according to the PIM and its parameters, the mining subsidence surface deformation basin is predicted and obtain the surface deformation field of mining subsidence. Robust experiments show that QPSO has a certain ability to resist random errors and gross errors. The results of engineering application show that the mining area 3D deformation monitoring method proposed in this paper has certain engineering application value.

A multi‐objective distance vector‐hop localization algorithm based on differential evolution quantum particle swarm optimization

SummaryWireless sensor networks (WSNs) have actively been considered in vast amount of applications in fields of science and engineering. The node location technology is one of the most critical technologies of WSNs. Aiming at the problem of distance vector‐hop (DV‐HOP) algorithm's excessive estimation error, we propose in this article a multi‐objective DV‐HOP localization algorithm based on differential evolution quantum particle swarm optimization (DQPSO‐DV‐HOP). First, the set of anchor nodes generated during the deployment phase that would cause large errors is eliminated, and a correction factor is introduced to modify the average hop distance to reflect the actual situation of the network better. In the node localization phase, the objective function we propose is optimized under a combination of the DE and QPSO algorithms, so the estimated results of unknown nodes are optimized and modified by using the QPSO algorithm of fast convergence, which is easy to converge to the optimal global value. Simulation results show that the localization stability, accuracy, and convergence given by the proposed DQPSO‐DV‐HOP algorithm are better than other schemes. High precision positioning algorithm can improve the accuracy of energy consumption monitoring and provide more accurate data for energy saving management.

Rotating machinery weak fault signal detection method based on QPSO and stochastic resonance

Due to the weak early vibration signal of rotating machinery, which is nonlinear and non-stationary, the traditional filtering method has some limitations. To this end, a weak signal detection method combining Quantum Particle Swarm Optimization (QPSO) and Stochastic Resonance (SR) is proposed. First, according to the adiabatic approximation theory, make the original signal meet the SR requirements, and optimize the structural parameters of the SR through QPSO; secondly, the signal-to-noise ratio is used as the objective function to make the input signal and the output signal achieve the best match; finally, simulation and experimental data are used to verify the feasibility of the proposed method. Comparative analysis shows that the proposed method increases the amplitude by 46% compared with the characteristic frequency obtained by the PSO optimized SR model.

Artificial neural network based on quantum superposition and its application

The artificial neural network (ANN), as a powerful data analytical tool, has played a major role in facial recognition, nature language processing (NLP), etc. However, the efficiency of classical networks is seriously challenged by big data, however, the development of quantum theory can be combined with ANN to help overcome this problem. In this paper, we propose a quantum neural network, in which the quantum particle swarm optimization (QPSO) is used to optimize parameters. To analysze the system performance, it has been applied to function fitting and data classification and it is shown by classical simulation and experimentation that the proposed model is more efficient and powerful than ANN for a lot of experimental tasks. We hope that this scheme can be implemented on a quantum computer in the future.

The Prediction of Pile Foundation Buried Depth Based on BP Neural Network Optimized by Quantum Particle Swarm Optimization

Due to the fluctuation of the bearing stratum and the distinct properties of the soil layer, the buried depth of the pile foundation will differ from each other as well. In practical construction, since the designed pile length is not definitely consistent with the actual pile length, masses of piles will be required to be cut off or supplemented, resulting in huge cost waste and potential safety hazards. Accordingly, the prediction of pile foundation buried depth is of great significance in construction engineering. In this paper, a nonlinear model based on coordinates and buried depth of piles was established by the BP neural network to predict the samples to be evaluated, the consequence of which indicated that the BP neural network was easily trapped in local extreme value, and the error reached 31%. Afterwards, the QPSO algorithm was proposed to optimize the weights and thresholds of the BP network, which showed that the minimum error of QPSO-BP was merely 9.4% in predicting the depth of bearing stratum and 2.9% in predicting the buried depth of pile foundation. Besides, this paper compared QPSO-BP with three other robust models referred to as FWA-BP, PSO-BP, and BP by three statistical tests (RMSE, MAE, and MAPE). The accuracy of the QPSO-BP algorithm was the highest, which demonstrated the superiority of QPSO-BP in practical engineering.

Energy consumption optimization of processor scheduling for real-time embedded systems under the constraints of sequential relationship and reliability

Execution time, reliability, and energy consumption are the three main performance parameters of processor scheduling for real-time embedded systems. It is very meaningful to optimize the energy optimization of processor scheduling to satisfy the requirements on time limit and reliability. This paper tries to optimize the energy consumption of the processor under three constraints: the partial ordering relations between task modules, the time limit, and the reliability. Based on directed acrylic graph (DAG) and quantum particle swarm optimization (QPSO), two scheduling algorithms were developed for the problem, namely, DAG_QPSO_I and DAG_QPSO_II. The two algorithms were compared with each other, and with other intelligent algorithms. The results show that the proposed algorithms are superior in optimization effect and efficiency, DAG_QPSO_I makes energy consumption more efficient than DAG_QPSO_II, and DAG_QPSO_II meets stricter requirements on time limit and reliability than DAG_QPSO_I.

Bigradient neural network-based quantum particle swarm optimization for blind source separation

<p><span id="docs-internal-guid-df1e3816-7fff-2396-860a-693df6c8ad2e"><span>An independent component analysis (ICA) is one of the solutions of a blind source separation problem. ICA is a statistical approach that depends on the statistical properties of the mixed signals. The purpose of the ICA method is to demix the mixed source signals (observation signals) and rcovering those signals. The abbreviation of the problem is that the ICA needs for optimizing by using one of the optimization approaches as swarm intelligent, neural neworks, and genetic algorithms. This paper presents a hybrid method to optimize the ICA method by using the quantum particle swarm optimization method (QPSO) to optimize the Bigradient neural network method that applies to separate mixed signals and recover sources signals. The results of an implement this work prove that this method gave good results comparing with other methods such as the Bigradient neural network and the QPSO method, based on several evaluation measures as signal-to-noise ratio, signal-to-distortion ratio, absolute value correlation coefficient, and the computation time.</span></span></p>

Research and application of neural network for tread wear prediction and optimization

The wheel tread wear of heavy haul freight car in operation leads to shortened wheel turning period, reduced operation life, and poor train operation performance. In addition, wheel rail wear is a complex non-linear problem that integrates multiple disciplines. Thus, using a single physical or mathematical model to accurately describe and predict it is difficult. How to establish a model that could accurately predict wheel tread wear is an urgent problem and challenge that needs to be solved. In this paper, a tread wear prediction and optimization method based on chaotic quantum particle swarm optimization (CQPSO)-optimized derived extreme learning machine (DELM), namely CQPSO-DELM, is proposed to overcome this problem. First, an extreme learning machine model with derivative characteristics is proposed (DELM). Next, the chaos algorithm is introduced into the quantum particle swarm optimization algorithm to optimize the parameters of DELM. Then, through the CQPSO-DELM prediction model, the vehicle dynamics model simulates the maximum wheel tread wear under different test parameters to train and predict. Results show that the error performance index of the CQPSO-DELM prediction model is smaller than that of other algorithms. Thus, it could better reflect the influence of different parameters on the value of wheel tread wear. CQPSO is used to optimize the tread coordinates to obtain a wheel profile with low wear. The optimized wheel profile is fitted and reconstructed by the cubic non-uniform rational B-spline (NURBS) theory, and the optimized wear value of the tread is compared with the original wear value. The optimized wear value is less than the original wear value, thus verifying the effectiveness of the optimization model. The CQPSO-DELM model proposed in this paper could predict the wear value of different working conditions and tree shapes and solve the problem that different operating conditions and complex environment could have a considerable effect on the prediction of tread wear value. The optimization of wheel tread and the wear prediction of different tread shapes are realized from the angle of artificial intelligence for the first time.

Estimation of S-wave velocity from wire-line logs for organic-rich rocks

Existing shear-wave velocity prediction methods for organic-rich rocks have not simultaneously taken the effects of organic matter (kerogen) distribution and pore shape on the velocity into consideration. To solve this problem, the current paper proposes a shear-wave velocity prediction method for organic-rich rocks based on the Kuster–Toksöz (KT) rock physics model and chaotic quantum particle swarm optimisation (CQPSO) algorithm. In this method, the organic-rich rocks are considered equivalent to a mixture of minerals, kerogen particles and fluid-containing pores, in which kerogen particles and pores are both equivalent to ellipsoid-shaped inclusions. The effect of kerogen distribution and pore shape on velocity is described according to the change in the aspect ratio of ellipsoids. We used the error between the predicted and measured P-wave velocities to establish the inverse objective function, and introduced the CQPSO algorithm to calculate the equivalent kerogen particle and the equivalent pore aspect ratios. Based on this, the shear-wave velocity is predicted using inverted parameters. Compared with three existing single-parameter adaptive methods, the prediction results show that the proposed method is superior and can be satisfactorily implemented for laboratory measurement and on logging data.

Research on Distribution Network Fault Recovery Reconfiguration Considering Distributed Generations

In view of the increasing penetration rate of distributed power in the distribution network, the traditional fault recovery reconfiguration method of distribution network needs to be further improved. An economic optimization model of distribution network fault recovery reconfiguration considering different types of distributed generations is proposed. This model takes into account the minimum number of switching operations as target and considers the impact of network active loss on restorative reconfiguration. Integer coding quantum particle swarm optimization algorithm is applied to the fault recovery reconfiguration optimization model and a fault recoverability discriminant method is presented. This method can classify the fault network into recoverable power-lossing area and non-recoverable power-lossing area. Recovering the fault recovery method given by the recoverable power loss area, and restoring the power supply to the unrecoverable power loss area according to the distributed power supply condition connected to the area. The simulation results show that the proposed method can be applied to the fault recovery reconfiguration of distribution network with distributed power accurately and efficiently.

An optimized stacked diagnosis structure for fault diagnosis of wind turbine planetary gearbox

Fault diagnosis of the planetary gearbox (PGB) of wind turbines (WTs) plays an important role in the normal operation of WTs. Current studies commonly focus on the diagnosis of fault types of WT PGBs. Nevertheless, in addition to identifying the fault type, the current severity of the fault is also instructive for the maintenance and repair of WT PGBs. Thus, a novel optimized stacked diagnosis structure (OSDS) is proposed for the identification of fault type and severity. Compressed sensing is adopted to implement the compressed sampling of original vibration signals collected by the wireless sensor. Then, the compressed samples are input into first- and second-layer deep belief networks (DBNs) for the separate identification of fault type and severity. In order to realize the best feature extraction performance of DBNs, every single DBN in the OSDS is optimized with the chaotic quantum particle swarm optimization (CQPSO) algorithm. For OSDS, which is a hierarchical diagnosis system, the misdiagnosis results of the first layer will bring irreversible influence to the diagnosis of the second layer. That is to say, an incorrect fault type diagnosis will mean that these signals are wrongly classified, making them unable to judge the severity of the fault. Because the first-layer DBN is optimized with PGB historical data and the CQPSO algorithm, it shows an excellent performance in identifying fault types. Therefore, the diagnostic performance of OSDS has not been affected by the absence of diagnosis, and still shows an excellent recognition performance of fault type and severity in the experiment. This verifies its excellent role in the fault diagnosis of WT PGBs.

Variable order fractional grey model and its application

The use of constant order differential equations to describe the evolution of complex systems is often unable to describe some of the changing characteristics of the systems accurately. Variable order fractional derivatives provide us with new tools to solve such problems. In this paper, the accumulation and derivative orders of the classic grey model are expanded from constants to functions, and a variable order fractional grey model is established to describe the evolution process of complex systems. Firstly, this paper defines the variable order fractional accumulation generation sequence. On the basis of this sequence, a variable order fractional derivative grey model is established, the parameters of the model are estimated using the least square method, and the quantum particle swarm optimization algorithm is used to solve the order of fractional derivative and accumulation. Sadik transform and Laplace transform are adopted to obtain the analytical solution of the new model. Lastly, the effectiveness of the new model is verified through four cases. Compared with other models, the variable order fractional model can describe the development process of complex systems more accurately.

Determinants of Tourism Stocks During the COVID-19: Evidence From the Deep Learning Models.

This paper examines the determinants of tourism stock returns in China from October 25, 2018, to October 21, 2020, including the COVID-19 era. We propose four deep learning prediction models based on the Back Propagation Neural Network (BPNN): Quantum Swarm Intelligence Algorithms (QSIA), Quantum Step Fruit-Fly Optimization Algorithm (QSFOA), Quantum Particle Swarm Optimization Algorithm (QPSO) and Quantum Genetic Algorithm (QGA). Firstly, the rough dataset is used to reduce the dimension of the indices. Secondly, the number of neurons in the multilayer of BPNN is optimized by QSIA, QSFOA, QPSO, and QGA, respectively. Finally, the deep learning models are then used to establish prediction models with the best number of neurons under these three algorithms for the non-linear real stock returns. The results indicate that the QSFOA-BPNN model has the highest prediction accuracy among all models, and it is defined as the most effective feasible method. This evidence is robust to different sub-periods.

Combined Optimization of Frequency Band and Time Segment Using Quantum Particle Swarm Algorithm for Brain-Computer Interfaces

Common spatial pattern (CSP) is a popular algorithm for spatial filtering and subsequent feature extraction in motor imagery-based (MI) brain-computer interface (BCI) systems. The performance of CSP, however, depends heavily on the subject-specific frequency band and time segment used for classifying mental tasks. Accurate selection of most informative frequency band and time segment poses a great challenge. In this study, quantum particle swarm optimization is proposed for sole selection of frequency band and joint selection of frequency band and time segment, which are realized by a wrapping approach, incorporating CSP for feature extraction and support vector machine for classification into the classification model. The classification error rate is used as the fitness function of quantum particle swarm optimization. The classification performance of quantum particle swarm optimization based CSP algorithm for joint selection of frequency band and time segment is evaluated by comparing with other three CSP algorithms using either fixed frequency band and time segment or fixed time segment and the frequency band selected by particle swarm optimization and quantum-behaved particle swarm optimization, on two MI data sets with different number of channels and trials. Experimental results suggest that the proposed algorithm outperforms the other three algorithms in terms of classification error rate. Across all subjects from the two data sets, the averaged error rate of the proposed algorithm was 7.45%, 2.97% and 2.05% lower than the CSP with fixed frequency band and time segment, that with selected frequency bands by particle swarm optimization and that with selected bands by quantum particle swarm optimization. The proposed algorithm can facilitate the real-world application of BCIs.

A study design for statistical learning technique to predict radiological progression with an application of idiopathic pulmonary fibrosis using chest CT images

BackgroundIdiopathic pulmonary fibrosis (IPF) is a fatal interstitial lung disease characterized by an unpredictable decline in lung function. Predicting IPF progression from the early changes in lung function tests have known to be a challenge due to acute exacerbation. Although it is unpredictable, the neighboring regions of fibrotic reticulation increase during IPF's progression. With this clinical information, quantitative characteristics of high-resolution computed tomography (HRCT) and a statistical learning paradigm, the aim is to build a model to predict IPF progression. DesignA paired set of anonymized 193 HRCT images from IPF subjects with 6–12 month intervals were collected retrospectively. The study was conducted in two parts: (1) Part A collects the ground truth in small regions of interest (ROIs) with labels of “expected to progress” or “expected to be stable” at baseline HRCT and develop a statistical learning model to classify voxels in the ROIs. (2) Part B uses the voxel-level classifier from Part A to produce whole-lung level scores of a single-scan total probability's (STP) baseline. MethodsUsing annotated ROIs from 71 subjects' HRCT scans in Part A, we applied Quantum Particle Swarm Optimization–Random Forest (QPSO-RF) to build the classifier. Then, 122 subjects' HRCT scans were used to test the prediction. Using Spearman rank correlations and survival analyses, we ascertained STP associations with 6–12 month changes in quantitative lung fibrosis and forced vital capacity. ConclusionThis study can serve as a reference for collecting ground truth, and developing statistical learning techniques to predict progression in medical imaging.

Fractal derivative fractional grey Riccati model and its application

Fractal geometry methods are widely used to describe the geometric characteristics of complex systems, statistical behaviors, and power-law characteristics of data results. In this study, the fractal derivative and fractional cumulative generating operators are introduced into the grey Riccati model to establish the fractal derivative and fractional grey Riccati model (FDFGRM), and the analytical solution of the model is obtained. At the same time, multi-objective quantum particle swarm optimization algorithm is used for parameter optimization. We consider China's cement production, natural gas production, and primary aluminum production as examples to verify the validity of the model. The results show that the fitting and testing effects of the FDFGRM are better than those of other models. Finally, FDFGRM is used to predict the future trends of these three cases.