Improved Quantum Particle Swarm Optimization Research Articles

Research on optimal scheduling of microgrid based on improved quantum particle swarm optimization algorithm

INTRODUCTION: With the large-scale integration of new energy into the grid, the safety and reliability of the power grid have been severely tested. The optimized configuration of micro power systems is a key element of intelligent power systems, playing a crucial role in reducing energy consumption and environmental pollution.
 OBJECTIVES: a power grid optimization scheduling model is proposed that comprehensively considers the issues of power grid operating costs and environmental governance costs
 METHODS: Using quantum particle swarm optimization method to optimize the objective function with the lowest system operating cost and the lowest environmental governance cost. In order to improve the search ability of the algorithm and eliminate the problem of easily getting stuck in local optima, the Levy flight strategy is introduced, and the variable weight method is used to update the particle factor to improve the optimization ability of the algorithm.
 RESULTS: The simulation results show that the improved quantum particle swarm optimization algorithm has strong optimization ability, and the scheduling model proposed in this paper can achieve good scheduling results in different scheduling tasks.
 CONCLUSION: （1）The improved particle swarm algorithm, in comparison to itspredecessor, boasts a greater degree of optimization accuracy, aswifter convergence rate, and the capability to avoid the algorithm'sdescent into the local optimal solution at a later stage of the process. （2）The proposed model can effectively reduce users’ electricity costs and environmental pollution, and promote the optimized operation of microgrids.

State of health estimation of lithium-ion batteries based on multi-health features extraction and improved long short-term memory neural network

Accurate state of health estimation of lithium-ion batteries is essential to enhance the reliability and safety of a battery system. However, the estimation accuracy based on a data-driven model is degraded by one health feature and incorrect hyper-parameters selection. This paper develops a battery state of health estimation method based on multi-health features extraction and an improved long short-term memory neural network. To accurately describe the aging mechanism of batteries, health features are extracted from battery data, such as time features, energy features, and incremental capacity features. The correlation between multi-health features and state of health is evaluated by the grey relational analysis. Aiming at the problem that the hyper-parameters of an neural network model are difficult to select, an improved quantum particle swarm optimization algorithm is developed to correctly obtain the hyper-parameters. The experimental results show that the mean absolute error, mean absolute percentage error, and root mean square error of this method are all within 1%, which is much lower than other methods, with high state of health estimation accuracy and robustness.

Trajectory Planning in Robot Joint Space Based on Improved Quantum Particle Swarm Optimization Algorithm

Trajectory planning is a crucial step in controlling robot motion. The efficiency and accuracy of trajectory planning directly impact the real-time control and accuracy of robot motion. The robot’s trajectory is mapped to the joint space, and a mathematical model of trajectory planning is established to meet physical constraints during motion and avoid joint coupling problems. To enhance convergence speed and avoid local optima, an improved quantum particle swarm optimization algorithm is proposed and applied to solve the mathematical model of robot trajectory planning. The trajectory planning in robot joint space is then tested based on the improved quantum particle swarm optimization algorithm. The results demonstrate that this method can replace the traditional trajectory planning algorithms and offers higher accuracy and efficiency.

Multi task dynamic edge–end computing collaboration for urban Internet of Vehicles

As the future trend, more and more vehicles access to the Internet of Vehicles, which means that a huge number of tasks of the vehicle terminals need to be transformed and completed on the network. Edge computing makes the tasks executed on the edge nodes near the terminal, but some vehicle terminals are at a relatively idle state and these additional computing resources are not utilized, causing great waste of resources. What is more, it is hard to highly and comprehensively satisfy the high real-time requirements of some tasks. In order to execute these tasks efficiently, we propose a dynamic edge–end computing collaboration architecture for urban IoV. In this architecture, edge nodes and vehicle terminals can cooperate with each other, which means tasks can be allocated more dynamically and flexibly. We evaluate the completion of the task by considering task latency and overhead, task transmission model, task priority, as well as edge node and vehicle terminal’s capacity when defining task comprehensive utility. Then weformulate the task allocation as an optimization problem and propose an improved quantum particle swarm optimization algorithm to solve the problem. Simulation results show that the proposed strategy have better task allocation utility than other strategies, which can effectively solve the multi task allocation problem.

Intelligent Optimal Control Strategy of Heat Pump System Based on Digital Twins

In the context of the digital transformation of the power grid, digital twins, as one of the key technologies to promote the digital and intelligent development of the power industry, are still in the theoretical research stage. Focusing on the application of digital twin technology in the power industry, an intelligent optimal control strategy for heat pump system (HPS) based on digital twin technology is proposed. Firstly, the DOE-2 model of HPS and the prediction model of building heat load are established. Secondly, based on the real-time monitoring data, the improved quantum particle swarm optimization algorithm is used for the rolling optimization of model parameters. Finally, the optimal control strategy of the HPS is formulated based on the comprehensive consideration of energy consumption, the number of startups and shutdowns, and the temperature control requirements of the HPS. The example results show that the digital twin model has high accuracy. Compared with the manual control method, the proposed optimal control strategy can effectively improve energy efficiency utilization and has a better control effect, providing technical support for the operation and management of HPS.

Motion planning for an under-actuated autonomous underwater vehicle based on fast marching nonlinear model-predictive quantum particle swarm optimization

The motion planner determines the autonomy of the autonomous underwater vehicle. However, many studies often ignore the characteristics of autonomous underwater vehicles and the underwater environment, resulting in unreasonable trajectories. To solve this problem, this paper plans the motion force through prediction and optimization to integrate the characteristics of the autonomous underwater vehicle. The fast travel algorithm is modified to take advantage of favorable currents and output arrival times, taking into account the peculiarities of the underwater environment. A novel objective function is proposed, which incorporates the arrival times to imply the requirements of obstacle avoidance and distance optimization. A penalty item is included in the objective function to avoid strong side flow. At the same time, the force and its changing rate limited in appropriate ranges are also included to balance energy consumption. In order to meet the high running time requirements in practical applications, the warm-start improved quantum particle swarm optimization is introduced into force optimization. Simulation studies are performed on a self-developed autonomous underwater vehicle. The method has good application effects in multi-island areas, ocean currents, and dynamic three-dimensional environments. Comparative simulations show that the planner plans shorter paths with good robustness and adaptability.

Investigation of the Optimal PID-Like Fuzzy Logic Controller for Ball and Beam System with Improved Quantum Particle Swarm Optimization

Fuzzy Logic Controllers (FLCs) are intelligent control methods, where membership functions and corresponding rules are defined to get a proper control signal. The parameters were defined for these controllers, and they are named as PID-like FLC since the input and output parameters are connected to the Fuzzy controller with integral and derivative action of the error signal to change the behavior/performance of FLC. In this research, three different rule sets for Fuzzy controllers; 3 × 3, 5 × 5, and 7 × 7 are used and parameters are optimized with; differential evolution, genetic algorithm, particle swarm optimization and quantum-behaved particle swarm optimization. In addition to these controllers, a novel algorithm named as improved quantum particle swarm optimization is proposed as a part of this research. The simulation and real-life implementation on the experimental set results of these controllers are discussed in this paper.

A wind power curtailment reduction strategy using electric vehicles based on individual differential evolution quantum particle swarm optimization algorithm

A wind power curtailment consumption strategy using electric vehicles (EVs) based on individual differential evolution quantum particle swarm optimization algorithm (IDE-QPSO) is proposed, with the objective of reducing the system’s wind curtailment in order to further improve the wind power consumption rate while effectively reducing wind power output fluctuation and amplitude. EV aggregators act as charging tariff setters, releasing dynamic time-of-use tariffs (DTOU) for EV clusters to respond to based on wind curtailment data accounted for by the dispatch center. This method first establishes an electric vehicle charging load model based on the travel chain theory and residents’ travel rules, then establishes an EV users autonomous response model based on the sensitivity of electric vehicle users to the charging prices. Second, a multi-objective optimization function is established based on the aforementioned model, which integrates wind power curtailment consumption and minimizes wind power output fluctuation and amplitude, and it is solved using an improved quantum particle swarm optimization algorithm. Finally, adequate simulation experiments show that this strategy can effectively smooth out the fluctuation of wind power output and improve the wind power consumption rate.

Risk Assessment of Deep Coal and Gas Outbursts Based on IQPSO-SVM.

Coal and gas outbursts seriously threaten the mining safety of deep coal mines. The evaluation of the risk grade of these events can effectively prevent the occurrence of safety accidents in deep coal mines. Characterized as a high-dimensional, nonlinear, and small-sample problem, a risk evaluation method for deep coal and gas outbursts based on an improved quantum particle swarm optimization support vector machine (IQPSO-SVM) was constructed by leveraging the unique advantages of a support vector machine (SVM) in solving small-sample, high-dimension, and nonlinear problems. Improved quantum particle swarm optimization (IQPSO) is used to optimize the penalty and kernel function parameters of SVM, which can solve the optimal local risk and premature convergence problems of particle swarm optimization (PSO) and quantum particle swarm optimization (QPSO) in the training process. The proposed algorithm can also balance the relationship between the global search and local search in the algorithm design to improve the parallelism, stability, robustness, global optimum, and model generalization ability of data fitting. The experimental results prove that, compared with the test results of the standard SVM, particle swarm optimization support vector machine (PSO-SVM), and quantum particle swarm optimization support vector machine (QPSO-SVM) models, IQPSO-SVM significantly improves the risk assessment accuracy of coal and gas outbursts in deep coal mines. Therefore, this study provides a new idea for the prevention of deep coal and gas outburst accidents based on risk prediction and also provides an essential reference for the scientific evaluation of other high-dimensional and nonlinear problems in other fields. This study can also provide a theoretical basis for preventing coal and gas outburst accidents in deep coal mines and help coal mining enterprises improve their safety management ability.

Multi-objective coordinated development paths for China's steel industry chain based on “water-energy-economy” dependence

The water-energy crisis seriously affects the sustainable development of China's steel industry chain. To achieve a coordinated development new path from the perspective of circular economy, it is necessary to analyze “water-energy-economy” dependency relationship of the steel products. This study analyzes a variety of steel products from the perspective of industry chain and simulates the “water-energy-economy” potential changes of products under different scenarios by developing a multi-objective optimization model. In this model, Random Forest (RF) and GEne Network Inference with Ensemble of trees (GEINIE3) algorithms are combined to evaluate the 2013–2019 “water-energy-economy” dependency relationships firstly. Then, improved Quantum Particle Swarm Optimization (QPSO) algorithm is applied to dynamically simulate potential changes of water, energy and economic performance in the steel industry chain under different scenarios, and to design an optimal development path from the perspective of optimizing economic performance within minimum water and minimum energy use constraints. Results firstly point out the current “water-energy-economy” triple dimension dependency relationship of China's steel industry is weak. Secondly, the “water-economy” dependence has changed from one-way dependence to two-way dependence, and the “energy-economy” relationship still shows a one-way dependence. Finally, when improving resource utilization rate, assigning priority to the reuse of scrap steel, while restricting pig iron and primary steel use, may help maximize the coordinated development of “water-energy-economy” in the steel industry chain. Policy implications are proposed based on the results and provided decision-making basis for the country and relevant enterprises to promote sustainable development of the steel industry chain.

Sensitivity Analysis Contact Reliability of VH-CATT Cylindrical Gear and Its Reliability with Material Strength Degradation

VH-CATT cylindrical gear is a new transmission mechanism. In order to study the influence of design parameters and material strength degradation of the VH-CATT cylindrical gear on its transmission reliability. The Kriging surrogate model based on improved quantum particle swarm optimization (QPSO) algorithm was used to establish the limit state equation of contact stress for the VH-CATT cylindrical gear based on the stress-strength interference theory. The state equation of contact stress of the VH-CATT cylindrical gear was obtained with higher precision. Also, the strength degradation model with gamma distribution was established by using the P-S-N curve to estimate the parameters of the strength degradation model with gamma distribution. The reliability sensitivity of the VH-CATT cylindrical gear was analyzed by improved first order second moment method (AFOSM) based on the limit state equation of contact stress of the VH-CATT cylindrical gear, the reliability of gear considering strength degradation was studied by using the improved first order second moment method (AFOSM). The analysis results show that the failure rate decreases with the increase of pressure Angle, tooth width, modulus and tooth radius. The failure rate increases with increasing torque. Strength degradation has a certain influence on the reliability of gear and opinion. And the reliability of the gear is lower than that of the opinion, Strength degradation has a greater influence on the failure probability of gear, at the same time, the reliability of the gear pair decreases with the strength degradation. After 15000 hours of operation, the reliability of the gear pair is only 0.63155.

Multi-step wind speed forecasting model using a compound forecasting architecture and an improved QPSO-based synchronous optimization

The intermittency and randomness of wind speed time series influence the forecasting accuracy. To figure out this problem and enhance the forecasting performance, a novel compound structure is developed for short-term wind speed forecasting. The developed compound method firstly eliminates the inherent noise from the original empirical wind speed time series using wavelet packet decomposition (WPD) and subsequently constructs appropriate input matrix by phase space reconstruction (PSR) for multi-kernel least square support vector machine (MKLSSVM). To take advantage of different kernel function, MKLSSVM with optimal combination of radial basis kernel function, polynomial kernel function and linear kernel function is constructed to make wind speed forecasting. Then, the proposed improved quantum particle swarm optimization algorithm (QPSO) based on the chaos initialization, Gaussian distribution local attraction points, precocity judgment, and disturbance operator, namely ADQPSO, is employed to optimize the decomposition level of WPD, reconstruction parameters of PSR, kernel parameters and weighted coefficients in MKLSSVM synchronously. To evaluate the forecasting performance of the proposed hybrid model, four sets of historical wind speed data samples from Weihai wind farm in China are utilized to make multi-step short-term wind speed forecasting tests. The experimental results illustrate that the proposed hybrid model outperforms the compared single and new recently developed forecasting models, thus, the proposed WPD-PSR-ADQPSO-MKLSSVM is an effective method for short-term wind speed forecasting.

Optimal Trajectory Planning for Minimizing Base Disturbance of a Redundant Space Robot with IQPSO

With the development of aerospace technology, the practical application of a free-floating redundant space robot has become more and more popular. The problem of minimizing base disturbance has been paid attention among academic researchers. If the space robot moves, it would have an impact on the pose of a base. The interference on a base should be reduced, which was caused by the movements of the space robot. In the paper, the simplified model of a redundant space robot has been described, which consists of a base and a 7-joint manipulator. Using the nonholonomic redundancy features, the pose of the base has been optimized planning. First, a set of kinematic equations of the redundant space robot was founded. Second, the 5-order polynomial function could be used for the parametric 7 joints. Third, on the basis of the pose requirements, a fitness function was defined. At last, the proposed improved quantum particle swarm optimization (IQPSO) algorithm was presented. The proposed IQPSO algorithm not only searched the optimal value easily but also had a good robust performance. The advantages could be shown through the numerical experiments, compared with the quantum-behaved particle swarm optimization (QPSO) algorithm, particle swarm optimization (PSO) algorithm, and simulated annealing particle swarm (SAPSO) algorithm. Then, the proposed IQPSO algorithm was used to optimize the fitness function of trajectory planning. By the simulation results, it could be confirmed that the proposed IQPSO algorithm searched the global optimal solution not only easily but also smoothly, compared with the QPSO, PSO, and SAPSO algorithms. The proposed approach was suitable for planning an optimal trajectory.

Optimal Integrated Energy System Planning With DG Uncertainty Affine Model and Carbon Emissions Charges

Integrated energy systems (IES) with cooling, heat, electricity, and natural gas have drawn significant interest recently as we embrace more sustainable energy a midst climate change. However, the uncertain outputs of distributed generators (DGs) make it challenging for IES planning while maintaining low-cost installation and operation under carbon emission constraints. To tackle the challenge, this work proposes an optimal planning model for IES considering both DG output uncertainties and carbon emission punishments. To reduce the conservatism of the widely-adopted interval and affine algorithms, an affine model based on the matrix form is first proposed to model the uncertain DG outputs. A tiered dynamic charging cost model is further developed to introduce and minimize carbon emissions with a punishment mechanism at the planning stage. Based on these two sub-models, an optimal IES planning model is proposed to simultaneously minimize the overall costs of investment, operation, and carbon emissions. To solve the multi-dimensional nonlinear model, an improved quantum particle swarm optimization (IQPSO) algorithm is introduced with enhanced global optimization ability. Simulation results on the IEEE 33-bus and 69-bus IES network have demonstrated that the proposed method can effectively reduce the impacts of DG uncertainty and carbon emissions at the planning stage of IES with a better long-term economy.

Joint offloading strategy based on quantum particle swarm optimization for MEC-enabled vehicular networks

With the development of the mobile communication technology, a wide variety of envisioned intelligent transportation systems have emerged and put forward more stringent requirements for vehicular communications. Most of computation-intensive and power-hungry applications result in a large amount of energy consumption and computation costs, which bring great challenges to the on-board system. It is necessary to exploit traffic offloading and scheduling in vehicular networks to ensure the Quality of Experience (QoE). In this paper, a joint offloading strategy based on quantum particle swarm optimization for the Mobile Edge Computing (MEC) enabled vehicular networks is presented. To minimize the delay cost and energy consumption, a task execution optimization model is formulated to assign the task to the available service nodes, which includes the service vehicles and the nearby Road Side Units (RSUs). For the task offloading process via Vehicle to Vehicle (V2V) communication, a vehicle selection algorithm is introduced to obtain an optimal offloading decision sequence. Next, an improved quantum particle swarm optimization algorithm for joint offloading is proposed to optimize the task delay and energy consumption. To maintain the diversity of the population, the crossover operator is introduced to exchange information among individuals. Besides, the crossover probability is defined to improve the search ability and convergence speed of the algorithm. Meanwhile, an adaptive shrinkage expansion factor is designed to improve the local search accuracy in the later iterations. Simulation results show that the proposed joint offloading strategy can effectively reduce the system overhead and the task completion delay under different system parameters.

Utilizing the Improved QPSO Algorithm to Build a WSN Monitoring System

This research uses the improved Quantum Particle Swarm Optimization (QPSO) algorithm to build an Internet of Things (IoT) life comfort monitoring system based on wireless sensing networks. The purpose is to improve the quality of intelligent life. The functions of the system include automatic basketball court lighting system, monitoring of infants’ sleeping posture and accidental falls of the elderly, human thermal comfort measurement and other related life comfort services, etc. On the hardware system of the IoT, this research is based on the latest version of ZigBee 3.0, which uses optical sensors, 3-axis accelerometers, and temperature/humidity sensors in the IoT perception layer. In the network transmission layer, the central network architecture is used for connection. In the application layer, we have designed a graphical interface for real-time values and information that can be read at any time and place using mobile devices. In this study, authors use the improved QPSO algorithm in the calculation part, so that the target can be effectively positioned outside the numerous surveillance data. This study uses various sensor data fusion technologies to make the IoT system becomes able to provide more extensive and even better services than ever before. In short, this research work has proven to be an effective way to reduce power consumption, improve medical quality and provide higher comfort for intelligent lift level.

Sensor cooperative scheduling method for low-altitude maneuvering target tracking in complex environment

PurposeContinuous and stable tracking of the low-altitude maneuvering targets is usually difficult due to terrain occlusion and Doppler blind zone (DBZ). This paper aims to present a non-myopic scheduling method of multiple radar sensors for tracking the low-altitude maneuvering targets. In this scheduling problem, the best sensors are systematically selected to observe targets for getting the best tracking accuracy under maintaining the low intercepted probability of a multi-sensor system.Design/methodology/approachFirst, the sensor scheduling process is formulated within the partially observable Markov decision process framework. Second, the interacting multiple model algorithm and the cubature Kalman filter algorithm are combined to estimate the target state, and the DBZ information is applied to estimate the target state when the measurement information is missing. Then, an approximate method based on a cubature sampling strategy is put forward to calculate the future expected objective of the multi-step scheduling process. Furthermore, an improved quantum particle swarm optimization (QPSO) algorithm is presented to solve the sensor scheduling action quickly. Optimization problem, an improved QPSO algorithm is presented to solve the sensor scheduling action quickly.FindingsCompared with the traditional scheduling methods, the proposed method can maintain higher target tracking accuracy with a low intercepted probability. And the proposed target state estimation method in DBZ has better tracking performance.Originality/valueIn this paper, DBZ, sensor intercepted probability and complex terrain environment are considered in sensor scheduling, which has good practical application in a complex environment.

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.

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.

Cross-project software defect prediction based on domain adaptation learning and optimization

Software defect prediction (SDP) is very helpful for optimizing the resource allocation of software testing and improving the quality of software products. The cross-project defect prediction (CPDP) model based on machine learning is first learned through the existing training data with sufficient number and defect labels on one project, and then used to predict the defect labels of another new project with insufficient number and fewer labeled data. However, its prediction performance has a large gap compared with the within-project defect prediction (WPDP) model. The main reason is that there are usually differences between the distributions of training data in different software projects, and it has a greater impact on the prediction performance of the CPDP model. To solve this problem, the kernel twin support vector machines (KTSVMs) is used to implement domain adaptation (DA) to match the distributions of training data for different projects. Moreover, KTSVMs with DA function (called DA-KTSVM) is further used as the CPDP model in this paper. Since the parameters of DA-KTSVM have an impact on its predictive performance, these parameters are optimized by an improved quantum particle swarm optimization algorithm (IQPSO), and the optimized DA-KTSVM is called as DA-KTSVMO. In order to confirm the effectiveness of DA-KTSVMO, some experiments are implemented on 17 open source software projects. Experimental results and analysis show that DA-KTSVMO can not only achieve better prediction performance than other CPDP models compared, but also achieve almost the same or better compared performance than WPDP models when the training sample data is sufficient. In addition, DA-KTSVMO can make better use of existing sufficient data knowledge and realize the reuse of defective data to improve the prediction performance of DA-KTSVMO.