Improved Particle Swarm Optimization Research Articles

A totally coupled multi time-scale framework containing full parameters online identification and SOC real-time estimation of lithium-ion battery based on a fractional order model

Fractional order model (FOM) is one of the most promising choices for lithium-ion battery simulation and internal states estimation due to its high modeling accuracy. However, online identifying the order and parameters of FOM is extremely tough, which also makes it difficult to be applied in complex and variable operating conditions. To address this issue, this paper proposes a totally coupled multi time-scale framework that contains full parameters online identification of FOM and state-of-charge (SOC) real-time estimation of lithium-ion battery. Firstly, a novel global optimization algorithm named improved particle swarm optimization is designed to update the order of FOM on macro-time scale with low computational burden. On the basis of the determined order, the rest parameters of FOM are further identified by forgetting factor recursive least square algorithm on micro-time scale. With those online identified parameters, adaptive extended Kalman filter is finally adopted to track battery SOC in every instant. The estimation accuracy, adaptability to different operating temperatures and battery aging, and robustness ability against uncertain initialization of the developed method is verified under Federal Urban Driving Schedule tests. The corresponding results demonstrate that the proposed method can realize accurate full parameters online identification of FOM and precise SOC real-time estimation against different temperatures, battery aging states and initialization. Using the developed method, the mean absolute error (MAE) and root mean square error (RMSE) of SOC estimation of the fresh battery under temperature ranging from 0 °C to 50 °C can be limited below 0.9 % and 1.1 %, respectively. Even if the SOC-OCV relationship is not timely updated, those two indexes of the aged battery achieved by the proposed method can still be controlled within 3 % under temperature ranging from 0 °C to 50 °C. Moreover, the comparison with other typical FOM-based estimation methods is also conducted, whose results indicate that the developed method has apparently superior comprehensive performance on estimation accuracy and adaptation.

Theoretical study of a Janus layered photonic structures based on improved particle swarm algorithm for detection of serum creatinine and glucose concentration

An improved particle swarm optimization (IPSO) algorithm is proposed to perform parameter optimization of the sensor which is realized by Janus layered photonic structures (LPS) based on the optical Tamm state (OTS) and defect layer, which can be used to detect serum creatinine (SCR) solution concentration (CSCR ) and glucose solution concentration (CG ) by adjusting the thickness and refractive index (RI) of the medium in the structure to control the peak and frequency changes of the transmission and absorption peaks. Due to the characteristic of Janus, the light incident from different directions can produce different effects. The OTS based on metal properties has sensitive and narrow absorption peaks when the light comes in forward propagation, its absorption characteristics allowing accurate measurement of CSCR with a high sensitivity (S) of 172 THz/RIU and a relevant quality factor (Q) of 651 and a lower detection limit (DL) of 2.3163 × 10−4. It has a significant Q of 4.5 × 104, DL of 4.825 × 10−5, and S of 11.7857 THz/RIU with its transmission characteristics which can be used to detect CG when the light comes in backward propagation. It can be said that the IPSO has a certain application prospect for the design of optical sensing.

Time-Optimal Trajectory Planning for Woodworking Manipulators Using an Improved PSO Algorithm

Woodworking manipulators are applied in wood processing to promote automatic levels in the wood industry. However, traditional trajectory planning results in low operational stability and inefficiency. Therefore, we propose a method combining 3-5-3 piecewise polynomial (composed of cubic and quintic polynomials) interpolation and an improved particle swarm optimization (PSO) algorithm to study trajectory planning and time optimization of woodworking manipulators. In trajectory planning, we conducted the kinematics analysis to determine the position information of joints at path points in joint space and used 3-5-3 piecewise polynomial interpolation to fit a point-to-point trajectory and ensure the stability. For trajectory time optimization, we propose an improved PSO that adapts multiple strategies and incorporates a golden sine optimization algorithm (Gold-SA). Therefore, the proposed improved PSO can be called GoldS-PSO. Using benchmark functions, we compared GoldS-PSO to four other types of PSO algorithms and Gold-SA to verify its effectiveness. Then, using GoldS-PSO to optimize the running time of each joint, our results showed that GoldS-PSO was superior to basic PSO and Gold-SA. The shortest running time obtained by using GoldS-PSO was 47.35% shorter than before optimization, 8.99% shorter than the basic PSO, and 6.23% shorter than the Gold-SA, which improved the running efficiency. Under optimal time for GoldS-PSO, our simulation results showed that the displacement and velocity of each joint were continuous and smooth, and the acceleration was stable without sudden changes, proving the method’s feasibility and superiority. This study can serve as the basis for the motion control system of woodworking manipulators and provide reference for agricultural and forestry engineering optimization problems.

Retracted: Application of Improved Particle Swarm Optimization Algorithm in Logistics Energy-Saving Picking Information Network

Retracted: Application of Improved Particle Swarm Optimization Algorithm in Logistics Energy-Saving Picking Information Network

Optimal sizing of battery-supercapacitor energy storage systems for trams using improved PSO algorithm

A hybrid energy storage system (HESS) of tram composed of different energy storage elements (ESEs) is gradually being adopted, leveraging the advantages of each ESE. The optimal sizing of HESS with a reasonable combination of different ESEs has become an important issue in improving energy management efficiency. Therefore, the optimal sizing method of battery-supercapacitor energy storage systems for trams is developed to investigate the optimal configuration of ESEs based on a constant power threshold. Firstly, the optimal sizing model of HESS taking size, mass, and cost of ESEs as a comprehensive objective function is established. Then, an improved particle swarm optimization (PSO) algorithm with a competition mechanism is developed for obtaining the optimal configurations of ESEs. Finally, Guangzhou Haizhu tram is taken as an example to verify the developed method. The results show that the optimal HESS with different ESEs has advantages in reducing the size, mass, and cost, and exerting the charging and discharging capacity of ESEs.

Multi-objective time-energy-impact optimization for robotic excavator trajectory planning

Single-objective optimal trajectory cannot adapt to the complex requirements of excavator construction. A comprehensive optimal trajectory planning method is proposed to optimize the working time, energy consumption, and operational impact of robotic excavators. Without fusing any performance indexes, a normalized multi-objective function and an improved particle swarm optimization algorithm are established to achieve a comprehensive optimization of multiple objectives, while considering joint angle, velocity, acceleration, and quadratic acceleration constraints. Typical deep pit excavation simulation and experimental results show that the multi-objective optimization method is feasible, can balance multi-objective constraints, and can avoid falling into extremely long working times or large impacts. This method offers a more efficient and effective solution for multi-objective trajectory planning and provides a method for planning excavation trajectories based on different operating scenarios and objectives.

Deployment Optimization of Self-Powered Sensors for Coverage Enhancement in the Internet of Things Sensing Layer

Coverage is an important indicator of the data acquisition and transmission of the Internet of Things(IoT) sensing layer. However, the sensor nodes are equipped with battery units, which results in limited coverage performance. Self-powered sensors provide a solution to handle energy constraint problems. In this paper, self-powered sensors are used as the sensing nodes, which could collect energy from the environment to supplement the battery unit while performing coverage tasks. A deployment optimization method of self-powered sensors is firstly proposed based on an improved Particle Swarm Optimization (PSO). In this method, the population is divided into homogeneous and heterogeneous clusters. Homogeneous clusters use the same search strategy as the PSO, while the heterogeneous clusters are further divided into multiple subgroups by the topology structure and using the improved communication strategy. On this basis, the position of self-powered sensors is obtained, which could achieve the desired coverage performance. Then, a local information-based critical node identification method is proposed. When the failed critical nodes are found, the redundant nodes around the critical nodes will work in place. The tolerance performance of coverage is enhanced. The results of simulation experiments show that the self-powered nodes are more uniformly distributed and the coverage is significantly improved, and the coverage could achieve 95.95%.

Three-Dimensional Path Planning of Deep-Sea Mining Vehicle Based on Improved Particle Swarm Optimization

Three-dimensional path planning is instrumental in path decision making and obstacle avoidance for deep-sea mining vehicles (DSMV). However, conventional particle swarm algorithms have been prone to trapping in local optima and have slow convergence rates when applied to underwater robot path planning. In order to secure a safe and economical three-dimensional path for the DSMV from the mining area to the storage base in connection with innovative mining system, this paper proposes a multi-objective optimization algorithm based on improved particle swarm optimization (IPSO) path planning. Firstly, we construct an unstructured seabed mining area terrain model with hazardous obstacles. Consequently, by considering optimization objectives such as the path length, terrain undulation, comprehensive energy consumption, and crawler slippage rate, we convert the path planning problem into a multi-objective optimization problem, constructing a multi-objective optimization mathematical model. Following that, we propose an IPSO algorithm to tackle the multi-objective non-linear optimization problem, which enables global optimization for DSMV path planning. Finally, we conduct a comprehensive set of experiments using the MATLAB simulation platform and compare the proposed method with existing advanced methods. Experimental results indicate that the path planned by the IPSO exhibits superior performance in terms of path length, terrain undulation, energy consumption, and safety.

Multi-Parameter Auto-Tuning Algorithm for Mass Spectrometer Based on Improved Particle Swarm Optimization.

Quadrupole mass spectrometers (QMS) are widely used for clinical diagnosis and chemical analysis. To obtain the best experimental results, mass spectrometers must be calibrated to an ideal setting before use. However, tuning the current QMS is challenging. Traditional tuning techniques possess low automation levels and rely primarily on skilled engineers. Therefore, in this study, we propose an innovative auto-tuning algorithm for QMS based on the improved particle swarm optimization (PSO) algorithm to automatically find the optimal solution of QMS parameters and make the QMS reach the optimal state. The improved PSO algorithm is combined with simulated annealing, multiple inertia weights, dynamic boundaries, and other methods to prevent the traditional PSO algorithm from the issue of a local optimal solution and premature convergence. According to the characteristics of the mass spectrum peaks, a termination function is proposed to simplify the termination conditions of the PSO algorithm and further improve the automation level of the mass spectrometer. The results of auto-calibration testing of resolution and mass axis show that both resolution and mass axis calibration could effectively meet the requirements of mass spectrometry experiments. By the experiment of auto-optimization testing of lens and ion source parameters, these parameters were all in the vicinity of the optimal solution, which achieved the expected performance. Through numerous experiments, the reproducibility of the algorithm was established as meeting the auto-tuning function of the QMS. The proposed method can automatically tune the mass spectrometer from its non-optimal condition to the optimal one, which can effectively reduce the tuning difficulty of QMS.

Research on fair residential critical peak price: Based on a price penalty mechanism for high-electricity consumers

We construct a fair residential critical-peak price (CPP) optimization model based on a penalty mechanism to address the peaking pressure on the grid and the cost of new energy abandonment caused by the yearly increase in residential electricity load and the significant increase in new energy capacity. According to the electricity consumption habits of different residential consumers, we use the K-means clustering method to identify and classify high-electricity consumers (HECs), low-electricity consumers (LECs), and normal-electricity consumers (NECs) during the critical peak and valley load hours respectively, and design a penalty mechanism of electricity price for HECs (PMEP-H) that penalizes HECs during the critical peak load hours, and compensates the total penalty amount to LECs during the critical peak load hours and HECs during the valley load hours, then calculate the residential electricity load after implementation of PMEP-H based on consumer psychology. The CPP optimization model is constructed by considering the constraints of consumer satisfaction with electricity, tariff penalty, compensation balance, penalty and compensation price, power balance and new energy-generating units output power, and the objective function of high comprehensive system efficiency represented by the difference between peak cost of thermal generation, the benefit of new energy consumption and benefit of the grid side. The model is then solved using an improved particle swarm optimization algorithm for a real residential area in a populated area in northern China. The results show that the developed model can connect the generation, grid and user sides through the electricity consumption tariff, which generates considerable benefits in both peak cutting and new energy consumption targets and can enhance the comprehensive benefits of the generation, grid and user sides system to achieve the supply-grid-load linkage, providing a reference for the improvement of the CPP mechanism in China.

A robust multiple Unmanned Aerial Vehicles 3D path planning strategy via improved particle swarm optimization

Path planning algorithms for Unmanned Aerial Vehicles (UAVs) are essential in various domains, such as search and rescue operations, agriculture, and delivery services. Nonetheless, finding optimal flight paths in complex environments with obstacles remains challenging. This paper proposes a novel path planning algorithm that uses a Nash equilibrium approach based on game theory to strike a balance between the exploitation and exploration capabilities of UAVs. The algorithm is designed in a simulated flight environment with obstacles, utilizing the self-awareness and group awareness coefficients from particle swarm optimization(PSO) as participants in the game theory model. The proposed path planning algorithm significantly improves the navigation of multiple UAVs in complex scenarios with obstacles by achieving a balance between their exploitation and exploration capabilities. Simulation experiments demonstrate that the proposed method surpasses traditional approaches, exhibiting an average improvement of 32.57%, 32.17%, and 29.33% in the algorithm convergence time, average flight distance, and average flight time, respectively. The significance of this research lies in its contribution to advancing UAV path planning algorithms. By integrating game theory and PSO, the proposed method optimizes the exploration and exploitation capabilities of UAVs, leading to improved navigation and resource utilization.

Bayesian Network Demand-Forecasting Model Based on Modified Particle Swarm Optimization

With the increasing variety of products, the increasing substitutability of products, and the trend of customized products, the volatility of market demand is increasing, which poses a challenge to make accurate demand forecasting. The Bayesian method is particularly promising and appealing when the data fluctuate greatly. This paper proposes a product-demand forecasting model based on multilayer Bayesian network, which introduces hidden layer variables and volatility factors to meet the time series connection and volatility of the demand data. However, most studies use sampling methods to estimate the parameters. We use Bayesian maximum a posteriori estimation to estimate the model parameters and introduce an improved particle swarm optimization algorithm (MPSO) to optimize the objective function. In order to increase the diversity of the particle population and accelerate the convergence, an adaptive particle velocity, position updating strategy, and nonlinear changing inertia weight are introduced in the algorithm. Finally, RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error) are used as the evaluation criterion to conduct experiments on six different datasets, and the experimental results are compared with the results of the ARIMA (autoregressive integrated moving average model) method and PSO algorithm. The experimental results show that the method has a good prediction effect. It provides a new idea for demand forecasting in the supply chain.

Data adjusting strategy and optimized XGBoost algorithm for novel insider threat detection model

With the growth of access to internal system resources, the insider threat problem is emerging and can bring immeasurable losses to enterprises. In order to detect the hidden threats and guide the formulation of enterprise management strategy, it is important to analyze and understand the employee behavior. Thus, we suggest a user behavior analysis system framework, which mainly includes data processing, user behavior modeling and results analysis. Data Adjusting (DA) strategy and optimized eXtreme Gradient Boosting (XGBoost) model are utilized with the aim of full analysis small amount of feature information. The strategy for detecting suspicious behavior can be the following. Firstly, select initial suspicious data, misclassification data retention and combination sampling. Secondly, for further behavior model construction, an improved Particle Swarm Optimization algorithm based on ethnic randomized particles (ERPSO), which introduces Gaussian white noise with adjustable intensity into acceleration coefficients is given for searching the optimal XGBoost parameters. In addition, based on the designed DA strategy and the proposed ERPSO algorithm, we have also compared the results of the proposed methods with the current state-of-the-art methods. Experimental results show that the XGBoost optimized by the ERPSO (ERPSO-XGBoost) model has comprehensive performance, which proves the rationality and effectiveness of the insider behavior analysis system framework. Through a comprehensive understanding of insider behavior, the obvious characteristic behavior is found to adjust the management strategy and guide the behavior modeling, so as to prevent more losses in time.

Research on optimal dispatch of distributed energy considering new energy consumption

In order to alleviate the problem of low proportion of new energy absorption in microgrids and reduce the operating cost of the system, this paper proposes an optimal dispatching method of microgrids considering new energy consumption. Firstly, a wind–solar diesel-storage microgrid model is established by combining data on wind energy, solar energy resources, and local loads in a specific region. Secondly, subsystems, including multiple distributed energy resources, are modeled and analyzed. Then, the cost and renewable energy absorption rate are taken as the objective function and their constraints are determined, and the particle swarm algorithm is used to solve the multi-objective. Finally, the capacity optimization and dispatch of the microgrid system, which includes wind, solar, diesel, gas, and energy storage, are obtained. This article also proposes an improved Particle Swarm Optimization (PSO) algorithm that exhibits better convergence properties compared to existing methods. The enhanced algorithm aligns with the rapid scheduling of individual microgrid units discussed in this paper, ensuring system economics while facilitating the integration of new energy sources. The effectiveness of the proposed method is verified with examples.

An improved particle swarm optimization based on age factor for multi-AUV cooperative planning

Multi-AUV planning of cooperative path represents the intelligence level of AUV formation system, which has attracted significant attention due to its flexibility and robustness. To enhance the speed and exploration capability of the multi-AUV autonomous path planner, this paper proposes an Age-based Multi-population Particle Swarm Optimization (AMPSO) for three-dimensional path planning of multiple AUVs. Firstly, reverse chaotic mapping is employed to enhance the initial distribution, enabling them to better adapt to the optimization process. In addition, the introduction of multiple populations helps to enhance the diversity within the population, thereby accelerating the convergence rate. Taking into account the search ability, the concept of an "age" coefficient is proposed to optimize the velocity updating formula to reduce the effect of inferior particles. Furthermore, a velocity-independent updating mode is suggested to enhance the population diversity by selecting different update modes. To evaluate the performance of the proposed algorithm, Monte Carlo simulations are conducted for AUV formations across two tracking tasks within the underwater environment established in this paper. The results demonstrate that the proposed algorithm outperforms other commonly used algorithms in terms of both speed and optimality, so the proposed AMPSO can be used for multi-AUV cooperative planning.

IEEE 802.11ax Meet Edge Computing: AP Seamless Handover for Multi-Service Communications in Industrial WLAN

IEEE 802.11ax, as a new generation of wireless local area network (WLAN) standard, has great development potential for multi-service access in industrial WLAN with its characteristics of low-cost, quick-deployment and high-efficiency. However, it still faces the problem of quick and efficient access point (AP) seamless handover for multi-service communications due to terminal mobility and channel degradation. To solve the problem, combining the characteristics of orthogonal frequency division multiple access (OFDMA) technology and multi-user multiple-input multiple-output (MU-MIMO) technology, this paper firstly proposes an edge computing based IEEE 802.11ax multi-service bearer mechanism by dynamically allocating channel resource units (RUs). Secondly, a multi-service AP seamless handover mechanism based on edge computing is proposed, which consists of a channel scanning delay optimization method based on virtual AP sentinel, a handover trigger judgment algorithm considering mobility and RU reallocation, a multi-objective optimized AP handover model considering load balancing, transmission rate optimization and handover cost minimization with service attributes. Finally, we propose a multi-service AP seamless handover algorithm based on improved particle swarm optimization (PSO) to get optimized handover making-decision. Simulation results show that the proposed handover mechanism can reduce the handover delay significantly while realizing seamless handover accurately in multi-service industrial WLAN.

Research on operation optimization problem of energy storage station in microgrid based on improved particle swarm optimization

In many microgrids with a lot of uncontrollable DGs, a well-planned operation of the energy storage station is an important guarantee for the stability and economy of the microgrid. To solve the operation optimization problem, the Chaos mechanism, differential evolution, and random inertia weight were introduced to propose the traditional PSO algorithm. A mathematical model for minimizing the running cost of Microgrid is established, and through MATLAB simulation, both algorithms are used to solve the same problem to observe the improvement of the algorithm. The experimental results show that the IPSO algorithm achieves performance improvement.

Reactive Power Optimization of Distributed PVConnected to Three-Phase Unbalanced Distribution Network

This study presents an approach to optimize reactive power in three-phase unbalanced distribution networks using an enhanced particle swarm optimization algorithm. The study analyzes the characteristics of reactive power optimization and establishes a reactive power optimization model for the distribution network using a multi-objective optimization method. Inequality constraints are utilized to determine the reactive power optimization model constraints for three-phase unbalanced distribution networks. The study applies an improved particle swarm optimization algorithm to solve the reactive power model and achieve optimization. The experimental results demonstrate that the proposed method considerably reduces the unused power of the optimized power network within 0-500ms and achieves a maximum reactive power optimization time of fewer than 8.2s, thereby enhancing the efficiency of reactive power optimization in distribution networks.

Design of Engine Cooling System Using Improved Particle Swarm Optimization Algorithm

Design of Engine Cooling System Using Improved Particle Swarm Optimization Algorithm

Multi-source monitoring data helps revealing and quantifying the excavation-induced deterioration of rock mass

There exist many high-steep rock slopes in Southwest China due to the construction of large-scale hydropower projects. The study on excavation-induced damage of high rock slopes is vital for disaster prevention and mitigation. Due to the complexity of high-steep rock slopes, assessing the excavation-induced deterioration of rock mass is often challenging. This study used the space-borne Interferometric synthetic aperture radar (InSAR) and multipoint displacement meters to monitor the slope displacements before and after the excavation. Based on the multi-source monitoring data and the parameter estimation method, we evaluated the changes in rock mass properties due to slope excavation. We employed an improved particle swarm optimization algorithm with dynamic topology and adaptive parameter adjustment and a neural network-based surrogate model in the parameter estimation. The degradation of rock mass strength and stiffness coincides with the increased sensitivity of slope deformation to rainfall infiltration. An in-depth analysis indicates that the absence of vegetation protection and excavation-induced deterioration of rock masses are responsible for the noticeable slope deformation during the rainy season. As slope excavation profoundly influences slope stability and deformation, we should pay closer attention to the excavation way and support style. This study also demonstrates the benefits of using multi-source monitoring data in slope deformation analysis. The findings can aid in comprehending the deformation evolution and instability mechanisms of rock slopes excavated at the hydropower sites, thereby contributing to the prevention and mitigation of landslide disasters.