Adaptive Particle Swarm Optimization Research Articles

An ensemble algorithm based on adaptive chaotic quantum-behaved particle swarm optimization with weibull distribution and hunger games search and its financial application in parameter identification

An ensemble algorithm based on adaptive chaotic quantum-behaved particle swarm optimization with weibull distribution and hunger games search and its financial application in parameter identification

A cosine adaptive particle swarm optimization based long-short term memory method for urban green area prediction

In the quest for sustainable urban development, precise quantification of urban green space is paramount. This research delineates the implementation of a Cosine Adaptive Particle Swarm Optimization Long Short-Term Memory (CAPSO-LSTM) model, utilizing a comprehensive dataset from Beijing (1998–2021) to train and test the model. The CAPSO-LSTM model, which integrates a cosine adaptive mechanism into particle swarm optimization, advances the optimization of long short-term memory (LSTM) network hyperparameters. Comparative analyses are conducted against conventional LSTM and Partical Swarm Optimization (PSO)-LSTM frameworks, employing mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) as evaluative benchmarks. The findings indicate that the CAPSO-LSTM model exhibits a substantial improvement in prediction accuracy over the LSTM model, manifesting as a 66.33% decrease in MAE, a 73.78% decrease in RMSE, and a 57.14% decrease in MAPE. Similarly, when compared to the PSO-LSTM model, the CAPSO-LSTM model demonstrates a 58.36% decrease in MAE, a 65.39% decrease in RMSE, and a 50% decrease in MAPE. These results underscore the efficacy of the CAPSO-LSTM model in enhancing urban green space area prediction, suggesting its significant potential for aiding urban planning and environmental policy formulation.

Identifying the plane position of the receiver coil in a multi‐transmitter IPT system through the identification of parameters

AbstractThe study introduces a method for identifying the location of receiver coils within a multi‐transmitter IPT system that utilizes shared transmitter coils. In contrast to the conventional receiver coil position recognition methods, this approach eliminates the need for additional detection coils and position sensors. Identifying the location of the receiver coil in a multi‐transmitter IPT system is achieved through the concurrent optimization of the transmitter coil and its electrical characteristics. The study presents the design of coil grouping and control mechanisms for a 3×3 multi‐transmitter. A mathematical model has been developed for the mutual inductance between receiver and transmitter coils on the X–Y plane. Adaptive cooperative particle swarm optimizer (ACPSO) facilitates the identification of mutual inductance and load parameters in a multi‐transmitter IPT system, positioning the receiver coil in a 2D plane using genetic particle swarm optimization (GAPSO) and a mutual inductance mathematical model. The discrepancy in accuracy between the receiver coil's test coordinates and the absolute coordinates remains below 3.5%.

Intelligent control algorithm for dynamic positioning control system

Introduction: The dynamic positioning system resists the environmental forces such as wind, wave and current acting on the ship through the thruster, so that the ship can remain in the position required by the sea level as much as possible, and the operation is very convenient. But its current dynamic positioning ability can not meet people's needs.Methods: A Kalman filter based on untracked optimization was designed for dynamic positioning control system. Then the intelligent control algorithm is designed for the dynamic positioning top-level controller and thrust optimal distribution controller, which occupy an important position in the system, namely the adaptive weight variation particle swarm optimization algorithm and thrust optimal distribution algorithm.Results and Discussion: The average position error of three degrees of freedom after filter processing is 1.53 m. Compared with other mainstream controllers, the mean root error of controllers based on adaptive weight variation particle swarm optimization in environment A and B is 2.295 and 1.8 m, respectively. In environment C, the controller based on thrust optimization allocation algorithm can get the optimal solution when the full rotary thruster reaches the 7 s and the channel thruster reaches the 4 s. The thrust exclusion zone is crossed at 46 s in environment D. In the dynamic positioning capability curve of the system, the experimental hull can balance the different environmental loads at all angles. In summary, the intelligent control algorithm proposed in this paper can effectively improve the positioning ability of the dynamic positioning control system and meet the needs of people for ship navigation today.

Vibration control by using active electromagnetic shunt damper

It is well-known that the traditional electromagnetic shunt damping (EMSD) techniques are limited by the damping force of electronic components and require a negative resistance (NR) shunt circuit to enhance performance. However, the NR shunt circuit could lead to the EMSD system being unstable. Addressing this, this study proposes an advanced control system that employs active control technology combined with EMSD for vibration control. We first developed a dimensionless mathematical model of the control system, which was then finely tuned using an adaptive simulated annealing particle swarm optimization algorithm. Subsequently, the relationship between control gain and optimal shunt circuit parameters was predicted using a BP neural network. Finally, the proposed Active-EMSD (AEMSD) was experimentally verified. The experimental results demonstrate that the proposed AEMSD not only surpasses traditional thresholds but also excels in isolating low-frequency vibrations. Compared to traditional EMSD, the proposed AEMSD showed improved effectiveness.

3D position deployment and performance optimization of mmWave UAV-assisted HetIoT under jamming condition

Heterogeneous Internet of Things (HetIoT) has received widespread attention due to its provision of various convenient services in fields such as smart cities, intelligent transportation, environmental monitoring and security systems. Due to HetIoT inherently demands high data rates, bandwidth, and low latency, the application of millimeter-wave (mmWave) unmanned aerial vehicle (UAV) as emergency aerial base station (ABS) providing services to HetIoT users has become a low-cost and efficient means. However, due to the sensitivity of mmWave to obstacles and jamming, guaranteeing network performance has become a pressing issue. This paper considers a mmWave UAV-assisted HetIoT under jamming conditions, where auxiliary ABSs serve multiple ground users (GUs) who generate a large amount of sensor data. We establish a coverage maximization problem under the constraints of signal-to-interference ratio (SIR) threshold, maximum power of ABSs and maximum number of GUs that the base station can serve, and propose a novel ABS hovering deployment algorithm M-HiAPSO that combines the artificial potential field (APF) method and the improved particle swarm optimization (PSO) algorithm in a hierarchical manner. Specifically, the multi-element APF method is used to characterize the horizontal force between nodes, combined with the improved hierarchical adaptive PSO algorithm to adjust the horizontal position of the ABS to obtain the optimal UAV hovering position and power allocation strategy. Numerical results show that the coverage rate reached 96.2% when the number of iterations was 283, and it can reach up to 99.6%.

Research on small target segmentation method in dual-energy X-ray images of coal and gangue based on APSO-C_Otsu

ABSTRACT Compared with wet coal preparation and traditional dry coal preparation, detection of coal and gangue with small particle size is a difficult problem for dual-energy X-ray separation technology. And small target segmentation is the premise of its detection, which is poor due to the fewer number of pixels and texture information in coal and gangue dual-energy X-ray images. So, the Otsu with crotch structure based on Adaptive Particle Swarm Optimization (APSO) for small target segmentation method is proposed, called after APSO-C_Otsu. Firstly, the Otsu with crotch structure is used to increase the contrast between small target and background. Meanwhile, the APSO algorithm was used to optimize the Otsu algorithm with crotch structure in order to improve its convergence speed and reduce its calculation amount. Finally, after binarization, the location of the targets are labeled based on the bwlabel algorithm. The results revealed that the APSO-C_Otsu algorithm could effectively segment the coal and gangue with a particle size of 5–30 mm, and was also applicable to the coal and gangue with the particle size larger than 30 mm, which was of great significance for accurate detection of coal and gangue and the improvement of coal utilization.

Improved Magnetic Resonance Image Reconstruction using Compressed Sensing and Adaptive Multi Extreme Particle Swarm Optimization Algorithm

One powerful technique that can offer a thorough examination of the body's internal structure is magnetic resonance imaging (MRI). MRI's lengthy acquisition times, however, may restrict its clinical usefulness, particularly in situations where time is of the essence. Compressed sensing (CS) has emerged as a potentially useful method for cutting down on MRI acquisition times; nevertheless, the effectiveness of CS-MRI is dependent on the selection of the sparsity-promoting algorithm and sampling scheme. This research paper presents a novel method based on adaptive multi-extreme particle swarm optimization (AMEPSO) and dual tree complex wavelet transform (DTCWT) for fast image acquisition in magnetic resonance. The method uses AMEPSO in order to maximize the sampling pattern and minimize reconstruction error, while also exploiting the sparsity of MR images in the DTCWT domain to improve directional selectivity and shift invariance. MATLAB software was used for simulation of the proposed method. In comparison with the particle swarm optimized-DTCWT (PSODTCWT) and DTCWT algorithms, respectively, the results demonstrated an improvement in the peak signal-to-noise ratio of 8.92% and 15.92% and a higher structural similarity index measure of 3.69% and 7.5%. Based on these improvements, the proposed method could potentially make high-quality, real-time MRI imaging possible, which might improve detection and treatment of medical conditions and increase the throughput of MRI machines.

Feature Extraction and Classification of Power Quality Disturbances Using Optimized Tunable-Q Wavelet Transform and Incremental Support Vector Machine

The widespread integration of renewable energy sources (RESs) into power systems using power electronics-based interface devices has led to a substantial rise in power quality (PQ) issues. There is an immediate requirement for effective monitoring, detection, and classification of power quality disturbances (PQDs) that is needed to take remedial measures and design planning of the system architecture. This study presents a hybrid approach with an objective for the feature extraction and classification of PQDs. The proposed hybrid approach is comprised of an optimized tunable-Q wavelet transform (OTQWT) for the feature extraction and incremental support vector machine (ISVM). A four-stage approach is suggested for the PQ detection and classification in this study. In the first stage, the various data are retrieved both in the form of synthetic data by mathematical formulations and real-time data with prototype design setup. In the second stage, regardless of the specified wavelet function, the PQD signals are decomposed into low-pass and high-pass sub-bands using the tunable-Q wavelet transform (TQWT). However, the utilization of default decomposition parameters to address nonstationary PQ signals may lead to information loss and reduced performance of the system. To avoid this limitation, an OTQWT as an enhanced technique to TQWT based on an Adaptive Particle Swarm Optimization (APSO) is suggested. A modified objective function based on the mean square error (MSE) is used to improve the decomposition process. In the third stage, an efficient classifier is suggested based on the ISVM. Lastly, to test and evaluate the performance of the proposed approach, twelve types of PQDs including noise and multiple occurrences are considered. The comparative analysis with other popular methods reflects the better performance of the proposed approach and justifies its use for PQ detection and classification purposes in real-time​ conditions.

Microstructure and deterioration mechanism of hydraulic concrete under variable temperature dry-wet cycles

In the variable water level area, hydraulic concrete structures are susceptible to variable temperature and alternating dry-wet cycles, especially for thin-walled structures. To elucidate alterations in the microstructure and strength of hydraulic concrete subjected to variable temperature dry-wet cycles, this paper firstly conducted a durability test on hydraulic concrete. Subsequently, CT scan images of the specimens are acquired during the variable temperature dry-wet cycle. Secondly, in view of the shortcomings of existing threshold methods for CT image pore segmentation, a new image segmentation method (RASLPSO-TV-Top-hat) is proposed by using Tensor voting and Top-hat technology, combined with a Reverse Adaptive Particle Swarm Optimization algorithm with Self-Learning ability. The evolution process of pore structure (microcracks) of hydraulic concrete under the variable temperature dry-wet cycles is captured. Thirdly, an in-depth analysis is conducted on the compressive strength and acoustic emission characteristics. The analysis shows that with the number of cycles increases, there is a discernible interconnection among the pore structures. This interconnection leads to a noteworthy reduction in the compressive strength of the concrete. Additionally, the cumulative number of Acoustic Emission ringing events exhibits an initial rise followed by a subsequent decline. The study successfully identifies the patterns governing the deterioration of strength and microstructure evolution in hydraulic concrete. The mechanism underlying the decline in performance of hydraulic concrete under variable temperature dry-wet cycles is elucidated. It provides a reliable and valuable basis for the safety assessment of hydraulic concrete structures in the variable water level area.

Transfer learning-based hybrid deep learning method for gas-bearing distribution prediction with insufficient training samples and uncertainty analysis

The availability of sample data, required for seismicity-based reservoir prediction using deep learning (DL), is often limited by well availability. To alleviate this problem, this study proposes a hybrid DL method combining unsupervised learning, transfer learning (TL), and intelligent optimization algorithms to predict gas-bearing distributions. First, three unsupervised learning methods were used to optimize multicomponent seismic attribute data during preprocessing and improve the sample data quality. Subsequently, a convolutional neural network (CNN) model was pre-trained using synthetic data, and the adaptive mutation particle swarm optimization (AMPSO) algorithm was used to optimize the hyperparameters of the CNN model during training to obtain the AMPSO-CNN model. Finally, the partial convolutional layer parameters of the pre-trained network were transferred, and the remaining parameters were adjusted using real data samples to obtain the TL-AMPSO-CNN model. The trained model was used for gas-bearing distribution prediction, and its stability was assessed using uncertainty analysis. The results confirmed that the hybrid DL model performed efficiently in gas-bearing prediction compared to other individual models. Thus, for datasets with insufficient training samples, the proposed scheme can be applied as a feasible alternative to predict gas-bearing distribution.

Adaptive Reactive Power Optimization in Offshore Wind Farms Based on an Improved Particle Swarm Algorithm

To address the reactive power optimization control problem in offshore wind farms (OWFs), this paper proposes an adaptive reactive power optimization control strategy based on an improved Particle Swarm Optimization (PSO) algorithm. Firstly, an OWF multi-objective optimization control model is established, with the total sum of voltage deviations at wind turbine (WT) terminals, active power network losses, and reactive power margin of WTs as comprehensive optimization objectives. Innovatively, adaptive weighting coefficients are introduced for the three sub-objectives, enabling the weights of each optimization objective to be adaptively adjusted based on real-time operating conditions, thus enhancing the adaptability of the reactive power optimization model to changes in operating conditions. Secondly, a Uniform Adaptive Particle Swarm Optimization (UAPSO) algorithm is proposed. On one hand, the algorithm initializes the particle swarm using a uniform initialization method; on the other hand, it improves the particle velocity update formula, allowing the inertia coefficient to adaptively adjust based on the number of iterations and the fitness ranking of particles. Simulation results demonstrate the following: (1) Under various operating conditions, the proposed adaptive multi-objective reactive power optimization strategy can ensure the stability of node voltages in offshore wind farms, reduce active power losses, and simultaneously improve reactive power margins. (2) Compared with the traditional PSO algorithm, UAPSO exhibits an approximately 10% improvement in solution speed and enhanced solution accuracy.

Adaptive knowledge transfer-based particle swarm optimization for constrained multitask optimization

Constrained multitask optimization (CMTO) problems are frequently encountered in practical applications. However, since the feasible regions of different tasks are difficult to match under constraints, it is hard to obtain and transfer effective knowledge among different tasks, resulting in poor convergence performance. To address this issue, a constrained multitask particle swarm optimization algorithm with knowledge transfer-based adaptive penalty function (CMTPSO-APF) is proposed to improve the convergence performance. The main contributions of CMTPSO-APF are three-fold. First, a dynamic particle topology is designed to improve the neighborhood similarity for different tasks with constrained conditions. Then, the particles with similar spatial structures can facilitate the knowledge transfer between different tasks. Second, a covariance matrix learning mechanism is proposed to obtain valid knowledge with constrained conditions. Then, the proposed method can promote the population to move in the direction of the optimal solution. Third, a knowledge transfer-based adaptive penalty function for constrained multitask optimization is designed to achieve the dynamical change of penalty factor according to the evolution state. Then, the adaptive penalty function can promote the transfer of effective knowledge and improve the convergence. Finally, some simulation experiments are carried out on the CMTO benchmark test suites and the practical application of the wastewater treatment process. The results indicate that CMTPSO-APF can improve the convergence performance.

Research on Annual Runoff Prediction Model Based on Adaptive Particle Swarm Optimization–Long Short-Term Memory with Coupled Variational Mode Decomposition and Spectral Clustering Reconstruction

Accurate medium- and long-term runoff prediction models play crucial guiding roles in regional water resources planning and management. However, due to the significant variation in and limited amount of annual runoff sequence samples, it is difficult for the conventional machine learning models to capture its features, resulting in inadequate prediction accuracy. In response to the difficulties in leveraging the advantages of machine learning models and limited prediction accuracy in annual runoff forecasting, firstly, the variational mode decomposition (VMD) method is adopted to decompose the annual runoff series into multiple intrinsic mode function (IMF) components and residual sequences, and the spectral clustering (SC) algorithm is applied to classify and reconstruct each IMF. Secondly, an annual runoff prediction model based on the adaptive particle swarm optimization–long short-term memory network (APSO-LSTM) model is constructed. Finally, with the basis of the APSO-LSTM model, the decomposed and clustered IMFs are predicted separately, and the predicted results are integrated to obtain the ultimate annual runoff forecast results. By decomposing and clustering the annual runoff series, the non-stationarity and complexity of the series have been reduced effectively, and the endpoint effect of modal decomposition has been effectively suppressed. Ultimately, the expected improvement in the prediction accuracy of the annual runoff series based on machine learning models is achieved. Four hydrological stations along the upper reaches of the Fen River in Shanxi Province, China, are studied utilizing the method proposed in this paper, and the results are compared with those obtained from other methods. The results show that the method proposed in this article is significantly superior to other methods. Compared with the APSO-LSTM model and the APSO-LSTM model based on processed annual runoff sequences by single VMD or Wavelet Packet Decomposition (WPD), the method proposed in this paper reduces the RMSE by 40.95–80.28%, 25.26–57.04%, and 15.49–40.14%, and the MAE by 24.46–80.53%, 16.50–59.30%, and 16.58–41.80%, in annual runoff prediction, respectively. The research has important reference significance for annual runoff prediction and hydrological prediction in areas with data scarcity.

Advanced forecasting of COVID-19 epidemic: Leveraging ensemble models, advanced optimization, and decomposition techniques

In the global effort to address the outbreak of the new coronavirus pneumonia (COVID-19) pandemic, accurate forecasting of epidemic patterns has become crucial for implementing successful interventions aimed at preventing and controlling the spread of the disease. The correct prediction of the course of COVID-19 outbreaks is a complex and challenging task, mainly because of the significant volatility in the data series related to COVID-19. Previous studies have been limited by the exclusive use of individual forecasting techniques in epidemic modeling, disregarding the integration of diverse prediction procedures. The lack of attention to detail in this situation can yield worse-than-ideal results. Consequently, this study introduces a novel ensemble framework that integrates three machine learning methods (kernel ridge regression (KRidge), Deep random vector functional link (dRVFL), and ridge regression) within a linear relationship (L-KRidge-dRVFL-Ridge). The optimization of this framework is accomplished through a distinctive approach, specifically adaptive differential evolution and particle swarm optimization (A-DEPSO). Moreover, an effective decomposition method, known as time-varying filter empirical mode decomposition (TVF-EMD), is employed to decompose the input variables. A feature selection technique, specifically using the light gradient boosting machine (LGBM), is also implemented to extract the most influential input variables. The daily datasets of COVID-19 collected from two countries, namely Italy and Poland, were used as the experimental examples. Additionally, all models are implemented to forecast COVID-19 at two-time horizons: 10- and 14-day ahead (t+10 and t+14). According to the results, the proposed model can yield higher correlation coefficient (R) for both case studies: Italy (t+10 = 0.965, t+14 = 0.961) and Poland (t+10 = 0.952, t+14 = 0.940) than the other models. The experimental results demonstrate that the model suggested in this paper has outstanding results in various kinds of complex epidemic prediction situations. The proposed ensemble model demonstrates exceptional accuracy and resilience, outperforming all similar models in terms of efficacy.

Height control strategy employing AWPSO-fuzzy PID control for electronically controlled air suspension system

Electronically controlled air suspension (ECAS) system has been extensively applied in vehicles to increase driving comfort, fuel economy, and operation safety. However, due to the nonlinear and hysteresis characteristics of the ECAS system, accurately controlling suspension height remains challenging. To tackle this problem, a bench test is conducted on the air spring and solenoid valve to establish their mathematical model for their characteristics. Based on this, a mathematical model of the ECAS system is developed by integrating suspension kinematics and dynamics theories. Then, a height control strategy is devised utilizing a fuzzy proportional-integral-derivative (PID) algorithm, enabling precise control of the vehicle’s body height. Based on this, an adaptive weight particle swarm optimization (AWPSO) algorithm is utilized to optimize the output range of the fuzzy controller. The effectiveness of the proposed model and control strategy is validated through both simulation tests and real-vehicle testing under both no-load and full-load conditions. These tests confirm the proposed approach’s effectiveness in achieving precise suspension height control in ECAS systems.

Design of Intelligent Control Systems for Layered Water Injections in Oilfields

With rapid socio-economic growth and increased energy demand, exploration and exploitation of oil and gas resources have become crucial. Long-term exploitation leads to problems such as pressure drop and production reduction in oil fields, and water injection technology has become a common method to improve these problems. The traditional direct water injection for oil extraction has problems such as high injection cost and low oil recovery efficiency. Therefore, an intelligent control system for different oilfield reservoirs is needed. This study focuses on the layered water injection intelligent system based on advanced sensor technology, digital signal processing and intelligent algorithms. The article reviews the advantages of layered water injection system and the current research status, designs an intelligent control structure including hardware circuits and modular software processes, and adopts adaptive particle swarm optimization algorithm as the core of intelligent control.

An APSO-based TPA-BLSTM model for predicting ship motion in irregular waves using wave-series input

ABSTRACT Combining the bidirectional long short-term memory neural network and temporal pattern attention mechanism, a machine learning model based on multi-feature inputs is developed to predict ship motion in irregular waves. An adaptive particle swarm optimisation algorithm is proposed to adapt the parameters of neural network to different feature inputs. The study indicates that the improved particle swarm optimisation algorithm avoids the problem of poor adaptability of empirical parameters to different advance time. The combination of bidirectional long short-term memory neural network and temporary pattern attention mechanism further enhances the data mining ability. Compared with the prediction mode solely based on the ship motion input, the inclusion of wave elevation input can effectively improve the accuracy and advance time for ship’s motion prediction. Importantly, the prediction accuracy will improve with an increased number of inputs of wave probes, which are arranged along the wave propagation direction.

Research on adaptive particle swarm optimization particle filter target tracking algorithm in wireless sensor networks

AbstractWith regard to target tracking in wireless sensor networks, we are faced with problems like deficient occlusion handling and tracking failures during rapid movements due to complex and diverse circumstances. In order to effectively improve the accuracy of particle filter tracking caused by particle degradation, we propose an adaptive particle swarm optimization (APSO) particle filter algorithm. This algorithm uses particle filters to predict the target location in a particular area and introduces the particle swarm optimization (PSO) algorithm, of which both the evolutionary speed and the convergence accuracy are further improved by investigating the particle distribution through an entropy analysis, employing three different inertial weighting strategies and dynamic double mutation strategy, and exploiting the capabilities of the adaptive balancing algorithm in global and local searching. The simulation results show that the improved algorithm has a reduced root mean square error, shorter time consumption, faster speed, reduced target tracking error, and higher average success rate, so this algorithm exhibits sound real‐time performance and accuracy in terms of occlusion handling and tracking loss.

Two-level optimal scheduling of source–storage-load interactive distribution network based on particle swarm optimization algorithm

In response to the difficulties of grid integration and consumption of a high proportion of new energy generation, as well as the high pressure on traditional thermal power generation to regulate peak loads, this paper constructs a wind-solar-fire-water complementary power generation system with “dual energy storage” and proposes a two-layer scheduling optimization strategy for this system. The scheduling model’s higher layer uses the system’s complementary power generation system’s maximum benefit as its objective function. The lower tier model has the minimization of the system’s cost of regulating load peaks as the objective function, and the model is solved by the improved adaptive immune particle swarm optimization algorithm considering various constraints. Taking the improved IEEE30 node system as an example for analysis, according to the operation of the original power system and the different characteristics of pumped storage and storage power plants, pumped storage and energy storage power stations are gradually added to the system for scheduling comparative analysis, and different capacities are configured, respectively. Through the analysis of examples, it is verified that the proposed dual-storage joint scheduling optimization strategy can effectively improve the overall operation of the system, reduce the peaking pressure of thermal power units, and achieve the expected goal of maximizing economic benefits. It also improves the new energy consumption in the region and alleviates the thermal power peaking pressure.