Adaptive Particle Swarm Optimization Algorithm Research Articles

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.

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.

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.

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.

Soft Sensor Modeling of Self-Organizing Interval Type-2 Fuzzy Neural Network Based on Adaptive Quantum-Behaved Particle Swarm Optimization Algorithm

Soft Sensor Modeling of Self-Organizing Interval Type-2 Fuzzy Neural Network Based on Adaptive Quantum-Behaved Particle Swarm Optimization Algorithm

Survival risk prediction of gastric cardia cancer-based on a dynamic modular neural network

Gastric cardia cancer is a high-incidence malignant tumour, which seriously endangers human health and life safety. The patient prognosis of gastric cardia cancer is affected by diet, physical condition, regional environment, medical history and other factors. Traditional prediction methods cannot fully reflect the prognosis characteristics and survival risks of all patients. Therefore, this paper proposes a data-driven method for the survival risk of cardiac cancer based on an adaptive particle swarm optimization algorithm (APSO) and a dynamic modular neural network (DMNN). First, the article uses density clustering to cluster 293 patients’ blood characteristics and generate different sub-networks. Second, the weight is calculated through the APSO algorithm and the sub-network output is obtained by the integration algorithm. At last, the effectiveness of this network is verified through a 50% cross-validation of training sets and test sets. The results show that the survival prediction based on the APSO-DMNN data-driven method shows good classification performance and accuracy.

Adaptive Particle Swarm Optimization of a Photovoltaic System under Partial Shading

The solar photovoltaic (PV) energy is the most prevalent and popular source of energy. But the PV output characteristics are mainly depending on temperature and irradiance and are nonlinear in nature. Therefore, PV array characteristics greatly vary with change in the atmospheric condition. Under partial shading condition (PSC), PV modules will not receive the same level of incident solar irradiance throughout the system due to some obstructions such as: dust, cloudy weather, shadows of nearby objects: buildings, trees, mountains, birds etc… which causes mismatch in PV module characteristics of the PV array and losses arise in the entire PV configuration. Consequently, power extraction from the PV system is reduced and the PSC on the PV array can be minimized by the proper selection of PV configurations, physical relocation of the PV modules and maximum power point tracking techniques (MPPT) to overcome this problem. The present article studies and compares the MPPT based on the Adaptive particle swarm optimization (APSO) algorithm under partial and completely shaded. The perturbation and observation (P&O) method is widely due to its simplicity and easy implementation but the Intelligent and hybrid control such as: fuzzy logic control (FLC) and adaptive neural fuzzy inference system (ANFIS) can track the MPP with better efficiency but in a long time compared to conventional approaches. In addition, these methods need big data for good results and the data problem is regulated with the evolutionary algorithms and searching the duty cycle (d) in a shorter time than FLC and ANFIS. The principle of PSO, grey wolf optimization (GWO), and APSO techniques is the search for a global solution, and it have good behaviour under PSC but APSO can be classified as best solution between the studied approaches. The simulation results, which are presented in MATLAB/Simulink software, show the effectiveness of the proposed APSO technique.

Stability-Considered Lane Keeping Control of Commercial Vehicles Based on Improved APF Algorithm

Regarding the lane keeping system, path tracking accuracy and lateral stability at high speeds need to be taken into account especially for commercial vehicles due to the characteristics of larger mass, longer wheelbase and higher mass center. To improve the performance mentioned above comprehensively, the control strategy based on improved artificial potential field (APF) algorithm is proposed. In the paper, time to lane crossing (TLC) is introduced into the potential field function to enhance the accuracy of path tracking, meanwhile the vehicle dynamics parameters including yaw rate and lateral acceleration are chosen as the repulsive force field source. The lane keeping controller based on improved APF algorithm is designed and the stability of the control system is proved based on Lyapunov theory. In addition, adaptive inertial weight particle swarm optimization algorithm (AIWPSO) is applied to optimize the gain of each potential field function. The co-simulation results indicate that the comprehensive evaluation index respecting lane tracking accuracy and lateral stability is reduced remarkably. Finally, the proposed control strategy is verified by the HiL test. It provides a beneficial reference for dynamics control of commercial vehicles and enriches the theoretical development and practical application of artificial potential field method in the field of intelligent driving.

Daily natural gas load prediction method based on APSO optimization and Attention-BiLSTM.

As the economy continues to develop and technology advances, there is an increasing societal need for an environmentally friendly ecosystem. Consequently, natural gas, known for its minimal greenhouse gas emissions, has been widely adopted as a clean energy alternative. The accurate prediction of short-term natural gas demand poses a significant challenge within this context, as precise forecasts have important implications for gas dispatch and pipeline safety. The incorporation of intelligent algorithms into prediction methodologies has resulted in notable progress in recent times. Nevertheless, certain limitations persist. However, there exist certain limitations, including the tendency to easily fall into local optimization and inadequate search capability. To address the challenge of accurately predicting daily natural gas loads, we propose a novel methodology that integrates the adaptive particle swarm optimization algorithm, attention mechanism, and bidirectional long short-term memory (BiLSTM) neural networks. The initial step involves utilizing the BiLSTM network to conduct bidirectional data learning. Following this, the attention mechanism is employed to calculate the weights of the hidden layer in the BiLSTM, with a specific focus on weight distribution. Lastly, the adaptive particle swarm optimization algorithm is utilized to comprehensively optimize and design the network structure, initial learning rate, and learning rounds of the BiLSTM network model, thereby enhancing the accuracy of the model. The findings revealed that the combined model achieved a mean absolute percentage error (MAPE) of 0.90% and a coefficient of determination (R2) of 0.99. These results surpassed those of the other comparative models, demonstrating superior prediction accuracy, as well as exhibiting favorable generalization and prediction stability.

AdaL-PSO A New Adaptive Algorithm for the Multi-Skilled Resource-Constrained Project Scheduling Problem

MS-RCPSP is a combinatorial optimization problem that has many practical applications, this problem has been proven to belong to the NP-hard class, the approach to solving this problem is to use algorithms to find approximate solution. This paper proposed a New Adaptive Local Particle Swarm Optimization algorithm for the MS-RCPSP problem. The solution for the class of NP-Hard problems is to find approximate solutions using metaheuristic algorithms. However, most metaheuristic-based algorithms have a weakness that can be fallen into local extreme after a number of evolution generations. In this paper, we adopted a new adaptive nonlinear weight update strategy based on fitness value and new neighborhood topology for Particle Swarm Optimization algorithm, thereby helping to prevent PSO from falling into local extremes. The new algorithm is called AdaL-PSO. A numerical analysis is carried out using iMOPSE benchmark dataset and is compared with some other early algorithms. Results presented suggest the prospect of our proposed algorithm.

Crashworthiness Analysis and Optimization of Thin-Walled Structures Based on the TSSA-LSSVM Surrogate Model

Thin-Walled Structures (TWS) play a crucial role in enhancing both the collision safety and lightweight characteristics of vehicles. Structural design and optimization have demonstrated significant potential in improving their crashworthiness and overall weight reduction. Several interconnected factors, including the cross-sectional shape and parameters of distinctive structural characteristics (known as induction grooves), impact the TWS’ resistance to collisions. Another critical aspect in the optimization of TWS design is striking the delicate balance between the surrogate model’s effectiveness and precision. Taking all these factors into account, this paper conducts an in-depth investigation into the cross-sectional shape, grooves, and other geometric attributes of various TWSs through a comparative analysis. It reveals that the thin-walled structure with a Double-Ribbed Rectangle cross-section (DR-TWS) proves to be the optimal choice, particularly in low-speed axial impact scenarios. Furthermore, we present an enhanced Least Squares Support Vector Machine (LSSVM) method, augmented with an adaptive [Formula: see text]-distribution Sparrow Search Algorithm (TSSA), referred to as the TSSA-LSSVM surrogate model. The optimized design of the DR-TWS is realized through the integration of the TSSA-LSSVM surrogate model with the adaptive hybrid multi-objective particle swarm optimization (AHMOPSO) algorithm. This paper not only provides substantial theoretical support but also offers valuable guidelines for the application of TWSs in the automotive industry.

Measurement of Soot Concentration in Burner Diffusion Flames through Emission Spectroscopy with Particle Swarm Optimization.

Measuring soot concentration in a burner flame is essential for an in-depth understanding of the formation mechanism and to abate its generation. This paper presents an improved emission spectroscopy (ES) method that uses an adaptive particle swarm optimization (APSO) algorithm for measuring the concentration of soot in methane burner flames. Experimental tests were conducted on a laboratory-scale facility under a methane flowrate ranging between 0.6 and 0.9 L/min. A comparison analysis of the soot concentration measured by the ES method, the improved emission spectroscopy (IES) method, and the thermocouple particle density (TPD) method (as a reference) was conducted. The ES method obtained a maximum absolute deviation of 0.84 ppm from the average soot concentration at the three measurement points compared to the TPD method, while that of the IES was only 0.09 ppm. The experimental results demonstrate that the proposed IES method can obtain a more accurate soot concentration of diffusion flames.

Energy management strategy for low hydrogen consumption in hybrid power systems consisting of dual fuel cells and a single lithium battery

ABSTRACT Low hydrogen consumption is an important objective for fuel cell power systems as it impacts both economic efficiency and continuous power supply. This paper addresses the optimization problem of minimizing hydrogen consumption in a hybrid power system comprising dual fuel cells and a single lithium battery. We introduce an energy management method for the multi-period synchronous optimization, incorporating a data processing module and power allocation module. The quasi-periodic loads are predicted using a modified gray-Markov chain model, while the optimization problem is solved using an adaptive constrained particle swarm optimization (ACPSO) algorithm. An initial swarm generation algorithm is introduced to enhance the effectiveness of ACPSO, and a power correction algorithm is integrated to address deviations between predicted and actual loads. The effectiveness and advantages in optimizing hydrogen consumption of the proposed strategy have been demonstrated through simulations and experiments.