Quantum-behaved Particle Swarm Optimization Research Articles

Homotopy-SQP coupled method for optimal control of far-distance nonplanar rapid cooperative rendezvous with multiple specific-direction thrusts

In this paper, the far-distance nonplanar rapid cooperative rendezvous with multiple specific-direction thrusts is investigated. In the optimization process, the indirect method is adopted. The homotopy method is used to smooth the dynamical equations. The solutions of the initial co-state variables of the energy-optimal problem are obtained by the quantum-behaved particle swarm optimization (QPSO) algorithm. Sequential quadratic programming (SQP) algorithm is coupled into the homotopy method to overcome the difficulty in nonsmooth integration when the thrust switches on & off frequently. The solutions of the fuel-optimal problem are obtained by the homotopy-SQP coupled method. The simulation results show the feasibility of the cooperative rendezvous with multiple specific-direction thrusts. The “suitable point” of normal thrust is investigated.

Analysis and application of functional connectivity in synchronic hybrid mental tasks for brain-computer interface

As a new type of human–computer interaction technology, brain-computer interface (BCI) systems can translate the electroencephalography (EEG) signals as control commands. In our previous study, the synchronous hybrid mental tasks have been proposed with higher classification accuracy. In this study, to analyze the superiority of synchronous hybrid mental tasks, the causal networks of synchronous hybrid mental tasks and single mental tasks are compared. The causal networks are based on functional connectivity, which is calculated by partial directed coherence. The networks of the synchronous hybrid mental tasks present different interaction patterns. To further improve the classification accuracy of EEG signals, a feature extraction method combining the causal network and common spatial pattern (CSP) is proposed. After combining as mixed feature vectors, they are selected by binary quantum-behaved particle swarm optimization. The optimal feature subsets are classified by extreme learning machine. This average result is 3.1% and 9.5% higher than CSP and causal flow, respectively. The superiority of hybrid experimental paradigm can be studied by the causal network, and the performance of BCIs is also improved.

A Comprehensive Diagnosis Method of Rolling Bearing Fault Based on CEEMDAN-DFA-Improved Wavelet Threshold Function and QPSO-MPE-SVM.

A comprehensive fault diagnosis method of rolling bearing about noise interference, fault feature extraction, and identification was proposed. Based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), detrended fluctuation analysis (DFA), and improved wavelet thresholding, a denoising method of CEEMDAN-DFA-improved wavelet threshold function was presented to reduce the distortion of the noised signal. Based on quantum-behaved particle swarm optimization (QPSO), multiscale permutation entropy (MPE), and support vector machine (SVM), the QPSO-MPE-SVM method was presented to construct the fault-features sets and realize fault identification. Simulation and experimental platform verification showed that the proposed comprehensive diagnosis method not only can better remove the noise interference and maintain the original characteristics of the signal by CEEMDAN-DFA-improved wavelet threshold function, but also overcome overlapping MPE values by the QPSO-optimizing MPE parameters to separate the features of different fault types. The experimental results showed that the fault identification accuracy of the fault diagnosis can reach 95%, which is a great improvement compared with the existing methods.

Optimal Sizing of Battery-Integrated Hybrid Renewable Energy Sources with Ramp Rate Limitations on a Grid Using ALA-QPSO

Higher penetration of variable renewable energy sources into the grid brings down the plant load factor of thermal power plants. However, during sudden changes in load, the thermal power plants support the grid, though at higher ramping rates and with inefficient operation. Hence, further renewable additions must be backed by battery energy storage systems to limit the ramping rate of a thermal power plant and to avoid deploying diesel generators. In this paper, battery-integrated renewable energy systems that include floating solar, bifacial rooftop, and wind energy systems are evaluated for a designated smart city in India to reduce ramping support by a thermal power plant. Two variants of adaptive-local-attractor-based quantum-behaved particle swarm optimization (ALA-QPSO) are applied for optimal sizing of battery-integrated and hybrid renewable energy sources to minimize the levelized cost of energy (LCoE), battery life cycle loss (LCL), and loss of power supply probability (LPSP). The obtained results are then compared with four variants of differential evolution. The results show that out of 427 MW of the energy potential, an optimal set of hybrid renewable energy sources containing 274 MW of rooftop PV, 99 MW of floating PV, and 60 MW of wind energy systems supported by 131 MWh of batteries results in an LPSP of 0.005%, an LCoE of 0.077 USD/kW, and an LCL of 0.0087. A sensitivity analysis of the results obtained through ALA-QPSO is performed to assess the impact of damage to batteries and unplanned load appreciation, and it is found that the optimal set results in more energy sustainability.

Fracture performance prediction of polyvinyl alcohol fiber-reinforced cementitious composites containing nano-SiO2 using least-squares support vector machine optimized with quantum-behaved particle swarm optimization algorithm

Based on least-squares support vector machine optimized using a quantum particle swarm optimization algorithm (QPSO-LSSVM), a prediction model was established to predict the fracture properties of polyvinyl alcohol fiber-reinforced cementitious composites (CCs) containing nano-SiO2 (PVA-CCNS) and improve its accuracy and effectiveness. Nineteen groups of measured data obtained from fracture performance tests on a three-point bending notched PVA-CCNS beam were selected for analysis and prediction. The prediction results of the QPSO-LSSVM model were compared with those of the least-squares support vector machine optimized with the particle swarm optimization algorithm, least-squares support vector machine and back-propagation neural network models. The simulation analysis results indicated that the goodness of fit (R2) values of the fracture energy, initial fracture toughness and unstable fracture toughness were 0.790, 0.940 and 0.950, respectively, for the QPSO-LSSVM prediction model. In addition, the fitting degree between the measured and predicted values of the QPSO-LSSVM prediction model was better than those of the other three models. The higher accuracy, better convergence, and robustness of the QPSO-LSSVM model than the other three models proves that the QPSO-LSSVM model is an optimal method for predicting the fracture performance of CCs. The proposed model can guide the mix proportion design of CC mixtures.

DMO-QPSO: A Multi-Objective Quantum-Behaved Particle Swarm Optimization Algorithm Based on Decomposition with Diversity Control

The decomposition-based multi-objective evolutionary algorithm (MOEA/D) has shown remarkable effectiveness in solving multi-objective problems (MOPs). In this paper, we integrate the quantum-behaved particle swarm optimization (QPSO) algorithm with the MOEA/D framework in order to make the QPSO be able to solve MOPs effectively, with the advantage of the QPSO being fully used. We also employ a diversity controlling mechanism to avoid the premature convergence especially at the later stage of the search process, and thus further improve the performance of our proposed algorithm. In addition, we introduce a number of nondominated solutions to generate the global best for guiding other particles in the swarm. Experiments are conducted to compare the proposed algorithm, DMO-QPSO, with four multi-objective particle swarm optimization algorithms and one multi-objective evolutionary algorithm on 15 test functions, including both bi-objective and tri-objective problems. The results show that the performance of the proposed DMO-QPSO is better than other five algorithms in solving most of these test problems. Moreover, we further study the impact of two different decomposition approaches, i.e., the penalty-based boundary intersection (PBI) and Tchebycheff (TCH) approaches, as well as the polynomial mutation operator on the algorithmic performance of DMO-QPSO.

An improved QPSO-SVM-based approach for predicting the milling force for white marble in robot stone machining

Milling force prediction is one of the most important ways to improve the quality of products and stability in robot stone machining. In this paper, support vector machines (SVMs) are introduced to model the milling force of white marble, and the model parameters in the SVMs are optimized by the improved quantum-behaved particle swarm optimization (IQPSO) algorithm. A set of online inspection data from stone-machining robotic manipulators is adopted to train and test the model. The overall performance of the model is evaluated based on the decision coefficient (R2), mean absolute percentage error (MAPE) and root mean square error (RMSE), and the results obtained by IQPSO-SVM are superior to those of the PSO-SVM model. On this basis, the relationship between the milling force of white marble and various machining parameters is explored to obtain optimal machining parameters. The proposed model provides a tool for the adjustment of machining parameters to ensure stable machining quality. This approach is a new method and concept for milling force control and optimization research in the robotic stone milling process.

Signal Subspace Reconstruction for DOA Detection Using Quantum-Behaved Particle Swarm Optimization

Spatial spectrum estimation, also known as direction of arrival (DOA) detection, is a popular issue in many fields, including remote sensing, radar, communication, sonar, seismic exploration, radio astronomy, and biomedical engineering. MUltiple SIgnal Classification (MUSIC) and Estimation Signal Parameter via Rotational Invariance Technique (ESPRIT), which are well-known for their high-resolution capability for detecting DOA, are two examples of an eigen-subspace algorithm. However, missed detection and estimation accuracy reduction often occur due to the low signal-to-noise ratio (SNR) and snapshot deficiency (small time-domain samples of the observed signal), especially for sources with different SNRs. To avoid the above problems, in this study, we develop a DOA detection approach through signal subspace reconstruction using Quantum-Behaved Particle Swarm Optimization (QPSO). In the developed scheme, according to received data, a noise subspace is established through performing an eigen-decomposition operation on a sampling covariance matrix. Then, a collection of angles randomly selected from the observation space are used to build a potential signal subspace on the basis of the steering matrix of the array. Afterwards, making use of the fact that the signal space is orthogonal to the noise subspace, a cost function, which contains the desired DOA information, is designed. Thus, the problem of capturing the DOA information can be transformed into the optimization of the already constructed cost function. In this respect, the DOA finding of multiple signal sources—that is, the multi-objective optimization problem—can be regarded as a single objective optimization problem, which can effectively reduce the probability of missed detection of the signals. Subsequently, the QPSO is employed to determine an optimal signal subspace by minimizing the orthogonality error so as to obtain the DOA. Ultimately, the performance of DOA detection is improved. An explicit analysis and derivation of the developed scheme are provided. The results of computer simulation show that the proposed scheme has superior estimation performance when detecting signals with very different SNR levels and small snapshots.

Path planning for autonomous underwater vehicles based on interval optimization in uncertain flow fields

The predictive uncertainty of ocean currents needs to be considered in the path planning of autonomous underwater vehicles (AUV) in oceanic environments. An interval optimization (IO) scheme is presented in this paper to plan time-optimal paths for AUVs operating in oceanic environments with dynamic and uncertain flow fields; interval currents are established to process the uncertainty of forecasted ocean flows, and the IO scheme is used to search for time-optimal paths in the interval current fields. The IO scheme is a two-layer framework: In the outer layer, quantum-behaved particle swarm optimization is used to generate and optimize candidate paths. In the inner layer, the interval response of the candidate path from the outer layer is calculated under interval current fields, and they are converted into a time-optimal fitness value in accordance with the possibility degree of interval numbers. The influence of predictive uncertainty is analyzed via simulation, and the robustness of the IO scheme is verified.

Application of hybrid binary tournament-based quantum-behaved particle swarm optimization on an imperfect production inventory problem

Application of hybrid binary tournament-based quantum-behaved particle swarm optimization on an imperfect production inventory problem

An improved OS-ELM based Real-time prognostic method towards singularity perturbation phenomenon

Reliable and comprehensive historical data are fundamental for data-driven based reliability prognostic. In practice, the degradation process of electronic products is usually accompanied with singularities caused by intermittent or transient interference, in the past, singularities data often need to be detected in advance or even be regarded as noise being eliminated or ignored, before estimation process. Such traditional ways would lead to waste of time and resources, decrease the accuracy of reliability prognostic as well. Real-time models are being introduced to capture complete degradation trend on time, overcome the limitations of early detection and increase forecasting performance, recently. In this study, a novel real-time fusion approach based on spline function and optimized online sequential extreme learning machine (OS-ELM) is developed to address the above singularity perturbation issue. As the approximation model, the cubic non - polynomial spline function could track the singularities’ feature by window-slide dataset and trigonometric polynomial, form the initial prediction cell frames. ELM is introduced as an effective estimation model with simple structure to save computing resources. To address the uncertainty issue caused by its inherent randomness of ELM model. Quantum behaved particle swarm optimization (QPSO) and optimally pruning method were applied to optimize the parameters of ELM hidden network, and redundant network neurons, respectively. In the verification stage, different experimental results show that the proposed fusion model could capture singularities’ characteristics, achieve better prognostic performance in terms of accuracy.

Hardware in the Loop Simulation of Control Optimal of DC Motor Base on Modified Quantum-Behaved Particle Swarm Optimization

Proportional, Integral, Dervative (PID) controller is a simple and very reliable controller, but to determine the optimal control system parameters on the PID controller (Kp, Ki, and Kd) is not easy. This paper aims to determine the optimal parameters of the PID controller using Modified Quantum Behave Particle Swarm Optimization (MQPSO) to regulate the speed control of a DC motor. MQPSO is a method that has particle search behavior that is more detailed than its predecessor methods, namely Particle Swarm Optimization (PSO) and Quantum-Behaved Particle Swarm Optimization (QPSO). The test is done through Software In the Loop Simulation (SILS) by representing a DC motor in the transfer function, after the optimal control parameters are obtained then applied to the Hardware In the Loop Simulation (HILS) test using the actual DC motor. The test results show that the PID controller that has been optimized using MQPSO results in achieving DC motor response under steady conditions and is faster than the PID controller that is optimized using Ziegler-Nichols (ZN), PSO and QPSO. The completion time obtained by MQPSO is 41.0201 ms, which is also faster than using QPSO 42.8276 ms, PSO 43.7008 ms and ZN 61.8571ms.

Determining model parameter from self-potential data using quantum-behaved particle swarm optimization

A new approach for quantitative analysis of self-potential (SP) data is introduced. In this paper, anomaly of SP is associated with simple geometric models such as a vertical cylinder, a horizontal cylinder and a sphere object. Then, in order to estimate the depth, the electric dipole moment, the anomaly body’s centre, the geometrical form factor and polarization of the anomaly, the method was developed and implemented. The development and implementation of the method is based on the global optimization concept. This method uses Quantum-behaved Particle Swarm Optimization (QPSO) algorithm to overcome the inversion problem on SP anomaly modelling. The QPSO algorithm was randomly tested on synthetic data which consist of different random noise levels. The result shows a close agreement between the assumed and the measured parameters. At last, the validity of the method was tested on real SP anomaly data and compared to the results given by other advanced inversion approaches.

A New Location Sensing Algorithm Based on DV-Hop and Quantum-Behaved Particle Swarm Optimization in WSN

In wireless sensor network, the location sensing of the sensor nodes is practical. If there is no location information of the sensor nodes, the perceived data would have no meaning. In recent years, the range-free location sensing algorithms have got great attention. DV-Hop localization algorithm is one of the important algorithm in range-free location algorithms. It has high efficiency, convenient operation and low energy consumption. However, the localization accuracy cannot meet the requirements in some applications. In this paper, a new localization method is proposed, which is based on DV-Hop and Quantum-behaved Particle Swarm Optimization (QPSO) algorithm. First, it deals with the high influence of average single jumping distance and then modifies the calculation of it in the DV-Hop algorithm. Second, in order to solve the problem of the coordinate optimization in the DV-Hop algorithm, this study chooses QPSO algorithm to optimize the unknown nodes’ coordinates. Simulation results show that the new method can improve the localization accuracy of the unknown nodes obviously in WSN.

A study on ship collision conflict prediction in the Taiwan Strait using the EMD-based LSSVM method.

Ship collision accidents are the primary threat to traffic safety in the sea. Collision accidents can cause casualties and environmental pollution. The collision risk is a major indicator for navigators and surveillance operators to judge the collision danger between meeting ships. The number of collision accidents per unit time in a certain water area can be considered to describe the regional collision risk However, historical ship collision accidents have contingencies, small sample sizes and weak regularities; hence, ship collision conflicts can be used as a substitute for ship collision accidents in characterizing the maritime traffic safety situation and have become an important part of methods that quantitatively study the traffic safety problem and its countermeasures. In this work, an EMD-QPSO-LSSVM approach, which is a hybrid of empirical mode decomposition (EMD) and quantum-behaved particle swarm optimization (QPSO) optimized least squares support vector machine (LSSVM) model, is proposed to forecast ship collision conflicts. First, original ship collision conflict time series are decomposed into a collection of intrinsic mode functions (IMFs) and a residue with EMD. Second, both the IMF components and residue are applied to establish the corresponding LSSVM models, where the key parameters of the LSSVM are optimized by QPSO algorithm. Then, each subseries is predicted with the corresponding LSSVM. Finally, the prediction values of the original ship collision conflict datasets are calculated by the sum of the forecasting values of each subseries. The prediction results of the proposed method is compared with GM, Lasso regression method, EMD-ENN, and the predicted results indicate that the proposed method is efficient and can be used for the ship collision conflict prediction.

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.

An improved Gaussian distribution based quantum-behaved particle swarm optimization algorithm for engineering shape design problems

In this article, an improved Gaussian distribution based quantum-behaved particle swarm optimization (IG-QPSO) algorithm is proposed to solve engineering shape design problems with multiple constraints. In this algorithm, the Gaussian distribution is employed to generate the sequence of random numbers in the QPSO algorithm. By decreasing the variance of the Gaussian distribution linearly, the algorithm is able not only to maintain its global search ability during the early search stages, but can also obtain gradually enhanced local search ability in the later search stages. Additionally, a weighted mean best position in the IG-QPSO is employed to achieve a good balance between local search and global search. The proposed algorithm and some other well-known PSO variants are tested on ten standard benchmark functions and six well-studied engineering shape design problems. Experimental results show that the IG-QPSO algorithm can optimize these problems effectively in terms of precision and robustness compared to its competitors.

Distillation process optimization: A screening-clustering assisted kriging optimization method

Economic optimization is an important engineering aspect of modern distillation. A screening-clustering assisted kriging optimization (SCAKO) method is proposed herein to optimize the economics of the distillation process. The SCAKO consists of a kriging surrogate model, an expected improvement sampling approach, a screening-clustering operation, and a quantum-behaved particle swarm optimization algorithm. The main feature of the SCAKO method is the combination of an effective search domain contraction approach and the kriging surrogate model. The insignificant sampled points are deleted from the dataset, and the remaining sampled points are divided into a series of clusters. The search domain is then divided into several sub-domains according to the information of the points in the clusters. Kriging surrogate model is constructed to represent the variation trend of the optimization objective in each sub-domain. Case studies were performed to validate the computational effectiveness and efficiency of the SCAKO.

Non-singleton interval type-2 fuzzy PID control for high precision electro-optical tracking system

In this paper, two non-singleton interval type-2 fuzzy PID (NIT2F-PID) controllers for the high precision electro-optical tracking system (ETS) are presented to improve anti-interference ability. Specifically, two types of non-singleton fuzzifiers, including type-1 (T1) and type-2 (T2), are considered to construct the proposed NIT2F-PID (N1IT2F-PID and N2IT2F-PID) controllers. Faced with the optimization problem of parameters, particle swarm optimization (PSO), quantum-behaved PSO (QPSO), weighted QPSO (WQPSO) and improved QPSO with adaptive coefficients (LTQPSO) are employed for comparison of convergence performance to optimize the parameters of controllers. In addition, to demonstrate the superiority of the proposed NIT2F-PID controllers, PID, singleton T1 fuzzy PID (ST1F-PID), singleton interval T2 fuzzy PID (SIT2F-PID), T1 non-singleton interval T2 fuzzy (N1IT2F) and T2 non-singleton interval T2 fuzzy (N2IT2F) controllers are also designed. All of these controllers are tested under the circumstances of step disturbance and sinusoidal disturbance. At last, a series of simulation analyses and experimental results explicitly show performance of proposed NIT2F-PID controllers are superior to their counterparts.

Evaluation of open-pollinated offspring in Cyclamen persicum using vegetative phenology models in a primitive breeding population

Abstract Cyclamen is commercially cultivated to produce the pot and garden flowering plants by sowing the seeds, and the number of leaves is an important trait for the beginning of the initiations of flower buds and flowering. The yield potential is affected by the life cycle of a plant and the plant breeders can have good decisions making with the prediction of plant phenology. In this study, a polynomial function was proposed for modeling behavior of cyclamen offspring during the vegetative growth. This modeling is based on information on environmental changes and plant morphology up to the flowering stage. For this purpose, 30 pots (individuals) from a 121-individual population, which were the same in the size, were considered for sampling of data. The data were recorded as time series that include temperature (°C), relative humidity (%), leaf width (mm) and number of leaves. The output of this model is the number of leaves and the recorded inputs are the time (growth cycle; days), temperature, relative humidity and leaf width. Using the Quantum-behaved Particle Swarm Optimization (QPSO) algorithm, the constant coefficients of the proposed function (linear model) was calculated to match the input and output values to each other. To illustrate the robustness and efficiency of model, the growth rates of all individuals were compared using this proposed model. The result of root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R 2 ) between the estimated and observed values for each individual showed that 63% of the tested open-pollinated (OP) population is marketable. Therefore, phenology model could be a good estimation of the vigor of the OP population for commercial production. It should be noted that in obtaining the model, only five individuals were used randomly as training data, and the obtained model was fitted to the others as test dataset without changing the coefficients. Furthermore, a Gaussian model of the whole dataset showed that the OP seeds of cyclamen could be utilized to produce the potted flowering cyclamen without any worry about non-uniformity of harvest for the market if the optimum temperature would be adjusted.