Quantum Particle Swarm Optimization Research Articles

Hybrid deep learning and quantum-inspired neural network for day-ahead spatiotemporal wind speed forecasting

Wind is an essential, clean and sustainable renewable source of energy; however, wind speed is stochastic and intermittent. Accurate wind power generation forecasts are required to ensure that power generation can be scheduled economically and securely. This work proposes a hybrid deep learning technique that incorporates a quantum-inspired neural network to predict wind speeds 24 h in advance. An innovative neural network technique is implemented by cascading parallel convolutional neural networks (CNNs) with a long short-term memory (LSTM) and a quantum-inspired neural network (QINN). The proposed hybrid model is optimized using two iterative loops: the outer loop is implemented by using quantum particle swarm optimization (QPSO) to tune the structure of model and other hyperparameters/parameters. The inner loop uses an Adam optimizer to tune the weights and biases of the proposed model. Spatiotemporal wind speeds at various locations provide the 2D input data. Simulation results reveal that the proposed method outperforms other methods for 24 h-ahead wind speed forecasting.

Dynamic Temperature Compensation of Pressure Sensors in Migratory Bird Biologging Applications

This article proposes an improved dynamic quantum particle swarm optimization (DQPSO) algorithm to optimize a radial basis function (RBF) neural network for temperature compensation of pressure sensors used in tracking and monitoring wild migratory birds. The algorithm incorporates a temperature-pressure fitting model that includes temperature rate of change and gradient reference terms. It also includes a loss function that considers fitting accuracy and complexity, thereby improving the robustness of the sensor for complex temperature variations. The calibration experiments revealed that after implementation, the average absolute error of the pressure sensor output during dynamic temperature changes was reduced from 145.3 Pa to 20.2 Pa. This reduction represents an 86% improvement over the commercial polynomial compensation method, and the DQPSO approach significantly outperformed traditional feedforward network models. Finally, the algorithm was deployed and verified in an embedded environment for low-power, high-precision, real-time pressure compensation during the tracking and monitoring of wild migratory birds.

Modeling construction and demolition waste quantities in Tanta City, Egypt: a synergistic approach of remote sensing, geographic information system, and hybrid fuzzy neural networks

A waste management strategy needs accurate data on the generation rates of construction and demolition waste (CDW). The objective of this study is to provide a robust methodology for predicting CDW generation in Tanta City, one of the largest and most civilized cities in Egypt, based on socioeconomic and waste generation statistics from 1965 to 2021. The main contribution of this research involves the fusion of remote sensing and geographic information systems to construct a geographical database, which is employed using machine learning for modeling and predicting the quantities of generated waste. The land use/land cover map is determined by integrating topographic maps and remotely sensed data to extract the built-up, vacant, and agricultural areas. The application of a self-organizing fuzzy neural network (SOFNN) based on an adaptive quantum particle swarm optimization algorithm and a hierarchical pruning scheme is introduced to predict the waste quantities. The performance of the proposed models is compared against that of the FNN with error backpropagation and the group method of data handling using five evaluation measures. The results of the proposed models are satisfactory, with mean absolute percentage error (MAPE), normalized root mean square error (NRMSE), determination coefficient, Kling–Gupta efficiency, and index of agreement ranging between 0.70 and 1.56%, 0.01 and 0.03, 0.99 and 1.00, 0.99, and 1.00. Compared to other models, the proposed models reduce the MAPE and NRMSE by more than 92.90% and 90.64% based on fivefold cross-validation. The research findings are beneficial for utilizing limited data in developing effective strategies for quantifying waste generation. The simulation outcomes can be applied to monitor the urban metabolism, measure carbon emissions from the generated waste, develop waste management facilities, and build a circular economy in the study area.

A multiple fault diagnosis approach for transformer based on double antenna RFID sensor and PSA-optimized LSTM

This work aims to realize multiple mechanical fault diagnosis for transformers in the incubation period. A double antenna radio frequency identification (RFID) sensor is used to obtain vibration signals generated by a transformer. The measured raw vibration signals have prominent nonlinear characteristics; besides, they are mixed with vast noises, such as electromagnetic interference, measurement disturbance and so on, making it challenging to identify distinguishable features from the measured data. The long short-term memory (LSTM), which exhibits satisfactory performance in dealing with large-scale nonlinear time-series signals, is adopted to extract features from high-dimensional raw signals. Because the parameters of the LSTM cell and support vector machine significantly determine the accuracy of diagnosis, these parameters are determined using the chaotic quantum particle swarm optimization algorithm. Moreover, the sparse periodical attention (PSA) mechanism is used to enhance the LSTM model’s performance by focusing on global feature learning and determining the input data length. The experimental results verify that the exploited RFID sensor realizes reliable data wireless transmission within 17.5 m. Moreover, the PSA-optimized LSTM approach achieves satisfactory trade-off between diagnosis accuracy and computation complexity while dealing with multiple mechanical fault diagnosis of a transformer in early stages.

A Quantum Particle Swarm Optimization Algorithm Based on Aggregation Perturbation

A quantum particle swarm hybrid optimization algorithm based on aggregation disturbance is proposed for inventory cost control. This algorithm integrates the K-means algorithm on the basis of traditional particle swarm optimization, recalculates the clustering center, initializes stagnant particles, and solves the problem of particle aggregation. Introducing chaos mechanism into the algorithm, changing the position of particles, enhancing their activity, and improving the algorithm's global optimization ability. At the same time, define the aggregation disturbance factor, determine the current state of particles, optimize speed and position to accelerate escape, and solve the problem of particles falling into local optima. Experiments show that M-IKPSO algorithm has strong stability, fast Rate of convergence and high accuracy compared with other algorithms, and the improvement effect is significant.

Prediction Model of Borehole Spontaneous Combustion Based on Machine Learning and Its Application

In order to quickly and accurately predict borehole spontaneous combustion danger and avoid borehole spontaneous combustion fires, a borehole spontaneous combustion prediction model combining the Hunger Games search optimization algorithm (HGS) and Random Forest (RF) algorithm was introduced. The number of trees and the minimum number of leaf nodes in RF were optimized by HGS. Based on the data obtained from the temperature rise experiment of spontaneous combustion characteristics in a Shandong mine laboratory, O2, CO, C2H4, CO/∆O2 and C2H4/C2H6 were selected as the input indexes for the prediction of borehole spontaneous combustion, and the spontaneous combustion temperature was selected as the output indexes to train the built model. The prediction performance and accuracy of the model were evaluated using four indexes: the mean absolute error (MAE), mean absolute percentage error (MAPE), root mean square error (RMSE) and coefficient of determination (R2). At the same time, the prediction results of the HGS-RF model were compared with those of the RF model, Sparrow search algorithm (SSA) optimization RF model, particle swarm optimization RF model (PSO) optimization RF model and quantum particle swarm optimization RF model (QPSO) optimization. The results showed that the MAE of the RF, SSA-RF, PSO-RF, QPSO-RF and HGS-RF model samples were 17.541, 15.7752, 12.5903, 6.8594 and 6.6921, respectively. MAPE was 13.81%, 10.9766%, 9.6802%, 4.5731% and 5.1536%, respectively. RMSE values were 21.5646, 15.2017, 17.0091, 11.9879 and 12.1691, respectively. The R2 values were 0.9043, 0.9315, 0.9266, 0.9668, and 0.9717, respectively. At the same time, the reliability of the HGS-RF model was supplemented by taking the test data of the Zhengjia1204 coal mining face as an example. Finally, the model was applied to the prediction of borehole spontaneous combustion in the Jinniu Coal Mine, Shanxi Province. The prediction results show that the HGS-RF model can predict the spontaneous combustion temperature of different boreholes quickly and accurately. The results show that the HGS-RF model is more universal and stable than other models, indicating that the HGS-RF model is more suitable for the prediction of borehole spontaneous combustion.

An Energy Focusing-Based Scanning and Localization Method for Shallow Underground Explosive Sources

To address the problem of slow speed and low accuracy for recognizing and locating the explosive source in complex shallow underground blind spaces, this paper proposes an energy-focusing-based scanning and localization method. First, the three-dimensional (3D) energy field formed by the source explosion is reconstructed using the energy-focusing properties of the steered response power (SRP) localization model, and the velocity field is calculated based on a multilayered stochastic medium model by considering the random statistical characteristics of the medium. Then, a power function factor is introduced to quantum particle swarm optimization (QPSO) to search for and solve the above energy field and to approach the real location of the energy focus point. Additionally, the initial population is constructed based on the logistic chaos model to realize global traversal. Finally, extensive simulation results based on the real-world dataset show that compared to the baseline algorithm, the focusing accuracy of the energy field of the proposed scheme is improved by 117.20%, the root mean square error (RMSE) is less than 0.0551 m, the triaxial relative error (RE) is within 0.2595%, and the average time cost is reduced by 98.40%. It has strong advantages in global search capability and fast convergence, as well as robustness and generalization.

Cloud computing resource load prediction based on improved VMD and attention mechanism

Dynamic adjustment of resource supply according to users’ resource load is one of the important technologies to achieve efficient management of cloud computing resources. In order to accurately obtain users’ demand for resource load in the future, based on quantum particle swarm optimization (QPSO), a prediction model QVMD_AM_LSTM was proposed to optimize variational mode decomposition (VMD) and to add attention mechanism to AM_LSTM. A comparative experiment was conducted on the open-source dataset cluster-trace-v2018 from Alibaba Cloud. The outcomes show that compared with LSTM, AM-LSTM, GRU-LSTM, Refined-LSTM, Stacked-LSTM and other existing prediction models, the mean square error of the QVMD_AM_LSTM model proposed in this article decreases by 8-14, and the correlation coefficient rises by 6%-11%. QVMD_AM_LSTM model has higher prediction accuracy.

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.

Application of Quantum Particle Swarm Optimization for task scheduling in Device-Edge-Cloud Cooperative Computing

Swarm intelligence and evolutionary algorithms (SI&EAs) have been widely applied to various fields. In this paper, we make the first attempt, to our best knowledge, to apply an SI&EA, Quantum Particle Swarm Optimization (QPSO), for addressing the task scheduling problem in Device-Edge-Cloud Cooperative Computing (DE3C) which is one of the most widespread and new computing paradigms. We first formulate the problem and propose a QPSO based method to solve the problem with a reasonable time. Then we summarize the existing variants of QPSO, which exploit various improvement schemes for QPSO. At last, we conduct simulated experiments to evaluate the performance of QPSO and its variants on solving the task scheduling problem of DE3C, and have the following findings. (1) QPSO outperforms several up-to-date heuristics and SI&EAs in both the user satisfaction and the resource efficiency. (2) Existing improvement methods have no appreciable effect on QPSO for solving large-scale problems. (3) The performance of an improvement for QPSO depends mostly on randomness of the offset added to particle movements.

Inventory model for perishable items for interval-valued price dependent demand and advance payment policy with parametric approach via particle swarm optimization

ABSTRACT Advance payment for replenishment and the lifetime of an item are the most vital issues in the business sector. With the consideration of these issues, the formulation of a non-deterministic inventory model in the interval environment is of great importance. In this work, two different non-deterministic inventory models are formulated along with a variable holding cost, variable demand, and advertisement frequency under an advance payment policy in the interval environment. In the first inventory model, a no-shortage situation is discussed, whereas in the second model, a stock-out scenario is adopted by partially backordering during modelling. The modelling framework for both problems is accomplished based on interval differential equations. The interval optimization problems related to both models are obtained utilizing parametric approach and interval mathematics. Then, using interval order relations and different types of quantum particle swarm optimization, both interval optimization problems are solved. By means of two examples, the authentication of both models is examined and the benefits of the proposed approach are demonstrated. The impact of deviations of various parameters on optimality is studied graphically through the performance of sensitivity analyses, and the work is concluded by providing potential management insights and future scopes.

A quantum particle swarm optimization-based optimal LQR-PID controller for load frequency control of an isolated power system

One of the characteristics of a robust power grid is minimal variations in its frequency to load change or loss in generating unit(s). From the perspective of optimal control theory, the issue of load frequency control in the context of the interconnected functioning of power systems is investigated in this work, and a novel load frequency controller is proposed for a single area isolated power network. This novelty incorporates all the primary characteristics of the solutions that are based on a mixture of optimal controller designs by establishing a linear quadratic regulator optimized with quantum particle swarm optimization to design a proportional integral derivative (PID) controller unlike the conventional PID controller designs that are based on a combined Ziegler-Nichols and root locus (ZN-RL) method and manual tuning. The simulation results of the proposed controller using MATLAB show its efficacy in not only ensuring that there is no steady-state error in terms of the system frequency with load changes but also in achieving smoother transients. Following these landmark achievements, a transfer function model of the resulting power grid is constructed. The outcome of the model reveals that the system transients have been improved while keeping the intended steady-state characteristics. Furthermore, it is observed that the proposed load frequency controller has the best performance when compared with the combined ZN-RL method and the manual PID designs. This, therefore, demonstrates the superiority of the proposed design for load frequency control in power systems.

A Novel Method for Parameter Identification of Renewable Energy Resources based on Quantum Particle Swarm–Extreme Learning Machine

Accurately determining load model parameters is of the utmost importance for conducting power system simulation analysis and designing effective control strategies. Measurement-based approaches are commonly employed to identify load model parameters that closely reflect the actual operating conditions. However, these methods typically rely on iterative parameter search processes, which can be time-consuming, particularly when dealing with complex models. To address this challenge, this paper introduces a parameter identification method for the generalized synthetic load model (SLM) using the Extreme Learning Machine (ELM) technique, with the aim of enhancing computational efficiency. Furthermore, to achieve better alignment with load response curves, a Quantum Particle Swarm Optimization (QPSO) algorithm is adopted to train the ELM model. The proposed QPSO-ELM-based SLM parameter identification method is subsequently evaluated using a standard test system. To assess its effectiveness, parameter sensitivity analysis is performed, and simulation results are analyzed. The findings demonstrate that the proposed method yields favorable outcomes, offering improved computation efficiency in load model parameter identification tasks.

Ultra-Short-Term Load Forecasting for Customer-Level Integrated Energy Systems Based on Composite VTDS Models

A method is proposed to address the challenging issue of load prediction in user-level integrated energy systems (IESs) using a composite VTDS model. Firstly, an IES multi-dimensional load time series is decomposed into multiple intrinsic mode functions (IMFs) using variational mode decomposition (VMD). Then, each IMF, along with other influential features, is subjected to data dimensionality reduction and clustering denoising using t-distributed stochastic neighbor embedding (t-SNE) and fast density-based spatial clustering of applications with noise (FDBSCAN) to perform major feature selection. Subsequently, the reduced and denoised data are reconstructed, and a time-aware long short-term memory (T-LSTM) artificial neural network is employed to fill in missing data by incorporating time interval information. Finally, the selected multi-factor load time series is used as input into a support vector regression (SVR) model optimized using the quantum particle swarm optimization (QPSO) algorithm for load prediction. Using measured load data from a specific user-level IES at the Tempe campus of Arizona State University, USA, as a case study, a comparative analysis between the VTDS method and other approaches is conducted. The results demonstrate that the method proposed in this study achieved higher accuracy in short-term forecasting of the IES’s multiple loads.

Offloading approach for mobile edge computing based on chaotic quantum particle swarm optimization strategy

Offloading approach for mobile edge computing based on chaotic quantum particle swarm optimization strategy

Dual Stage Speed Control of BLDC Motor Using Hybrid QPSO-CSO Technique

Systems for driving motors are frequently employed with renewable energy sources. By using cutting-edge methods and algorithms, the power converter controlling technology boosts performance. The switching state of the converter from the solar module’s generation unit is where the QPSO (Quantum Particle Swarm Optimization) technique is applied in the currently available work. The duty cycle depends on the swarm velocities, which indirectly depends on the QPSO parameters. Hence, the QPSO parameters inertia (W), and learning coefficients C1 and C2, were determined using the Cuckoo search algorithm (CSO). This approach helps to extract the most powerful solar panel output and continuously operate a BLDC motor performance increasing the power converter controlling technology by utilizing state-of-the-art techniques and algorithms. Every module of the suggested boost converter was examined by the experimental findings. Based on generation and load, a boost converter module is obtained. The Models for single-ended primary inductors (SEPIC) and zeta converters are contrasted with the suggested logic. The overall design model is done by using MATLAB/Simulink 2021a.

Quantum Particle Swarm Optimization for Task Offloading in Mobile Edge Computing

Mobile Edge Computing (MEC) deploys servers on the edge of the mobile network to reduce the data transmission delay between servers and mobile devices, and can meet the computing demand of mobile computing tasks. It alleviates the problem of computing power and delay requirements of mobile computing tasks and reduces the energy consumption of mobile devices. However, the MEC server has limited computing and storage resources and mobile network bandwidth, making it impossible to offload all mobile computing tasks to MEC servers for processing. Therefore, MEC needs to reasonably offload and schedule mobile computing tasks, to achieve efficient utilization of server resources. To solve the above problems, in this paper, the task offloading problem is formulated as an optimization problem, and Particle Swarm Optimization (PSO) and Quantum Particle Swarm Optimization (QPSO) based task offloading strategies are proposed. Extensive simulation results show that the proposed algorithm can significantly reduce the system energy consumption, task completion time, and running time compared to recent advanced strategies, namely, Ant Colony Optimization (ACO), Multi-Agent Deep Deterministic Policy Gradients (MADDPG), Deep Meta Reinforcement Learning-based Offloading (DRMO), Iterative Proximal Algorithm (IPA), and Parallel Random Forest (PRF).

Structural design optimization under dynamic reliability constraints based on probability density evolution method and quantum-inspired optimization algorithm

Dynamic-reliability-based design optimization (DRBDO) has been a promising approach for designing structures under dynamic excitations in the presence of uncertainties. This paper proposes an effective scheme for solving a class of DRBDO problems. The proposed scheme is based on the quantum particle swarm optimization (QPSO) algorithm, a quantum-inspired algorithm that utilizes quantum mechanisms to achieve better exploration and exploitation. During the optimization process, the probability density evolution method (PDEM) combined with the extreme value distribution strategy is employed to evaluate the structural dynamic reliability. Due to the high efficiency of the PDEM, the computational cost associated with reliability assessments can be considerably reduced. Numerical examples involving linear and nonlinear structures with different types of design variables are presented to demonstrate the effectiveness and efficiency of the proposed scheme.

Research on Adaptive 1DCNN Network Intrusion Detection Technology Based on BSGM Mixed Sampling.

The development of internet technology has brought us benefits, but at the same time, there has been a surge in network attack incidents, posing a serious threat to network security. In the real world, the amount of attack data is much smaller than normal data, leading to a severe class imbalance problem that affects the performance of classifiers. Additionally, when using CNN for detection and classification, manual adjustment of parameters is required, making it difficult to obtain the optimal number of convolutional kernels. Therefore, we propose a hybrid sampling technique called Borderline-SMOTE and Gaussian Mixture Model (GMM), referred to as BSGM, which combines the two approaches. We utilize the Quantum Particle Swarm Optimization (QPSO) algorithm to automatically determine the optimal number of convolutional kernels for each one-dimensional convolutional layer, thereby enhancing the detection rate of minority classes. In our experiments, we conducted binary and multi-class experiments using the KDD99 dataset. We compared our proposed BSGM-QPSO-1DCNN method with ROS-CNN, SMOTE-CNN, RUS-SMOTE-CNN, RUS-SMOTE-RF, and RUS-SMOTE-MLP as benchmark models for intrusion detection. The experimental results show the following: (i) BSGM-QPSO-1DCNN achieves high accuracy rates of 99.93% and 99.94% in binary and multi-class experiments, respectively; (ii) the precision rates for the minority classes R2L and U2R are improved by 68% and 66%, respectively. Our research demonstrates that BSGM-QPSO-1DCNN is an efficient solution for addressing the imbalanced data issue in this field, and it outperforms the five intrusion detection methods used in this study.

Active vibration control using nonlinear auto-regressive neural network to identify secondary channel

The power unit on board the ship generates periodic low-frequency vibration that affects the normal operation of the equipment on board, and the adaptive feedforward control algorithm can effectively suppress such harmful vibration noise. But the adaptive feedforward control algorithm needs to obtain the identification model of the secondary channels, and the frequency domain least squares method based on the linear Extended auto-regressive model (ARX) is difficult to obtain the identification model with nonlinear characteristics. The nonlinear auto-regressive model (NARX) adds nonlinear mapping layers to the topology of the ARX model to enhance the identification capability of the NARX model for complex systems. In this paper, a block diagram of the Fx-LMS feedforward control algorithm based on the NARX model is proposed, then the initial parameters of the NARX neural network are optimized using the Quantum Particle Swarm Optimization (QPSO) algorithm and the secondary channel is identified, and the identification results show that the accuracy of identifying the secondary channel using the NARX neural network is higher than that of the ARX model. The simulation and experimental results show that the vibration damping effect of the proposed method is better than the traditional Fx-LMS method for both single-line spectrum and multi-line spectrum periodic low-frequency disturbances, which provides a new method for the suppression of periodic low-frequency disturbances.