Weight Particle Swarm Optimization Research Articles

A Novel State of Health Estimation of Lithium-ion Battery Energy Storage System Based on Linear Decreasing Weight-Particle Swarm Optimization Algorithm and Incremental Capacity-Differential Voltage Method

A Novel State of Health Estimation of Lithium-ion Battery Energy Storage System Based on Linear Decreasing Weight-Particle Swarm Optimization Algorithm and Incremental Capacity-Differential Voltage Method

Study on the LQR Control of High-speed Elevator Car Horizontal Vibration Based on the Jumping Inertia Weight Particle Swarm Optimization

To reduce the horizontal vibration of a high-speed elevator car caused by the excitation of a guide rail more effectively, a differential magnetic suspension active guide shoe based on the principle of the differential magnetic suspension actuator is designed. Then the dynamic model of the active vibration damping system of the elevator car is established and an LQR controller is designed to reduce the horizontal vibration of the car. To search the weighting coefficient matrix of the LQR controller more efficiently, an algorithm named Jumping inertia Weight Particle Swarm Optimization (JWPSO) algorithm is proposed, and the frequently used fitness function verifies the optimization effect of the JWPSO algorithm. The weighting coefficient matrix of the LQR controller is optimized using the JWPSO algorithm. Finally, the impact of the JWPSO algorithm-optimized LQR controller is verified by MATLAB. The simulation result shows that the designed active vibration controller can effectively attenuate the horizontal vibration of the elevator car, and the control effect is significantly better than the LQR controller optimized by the GA algorithm and the H-inf controller optimized by LMI. This paper provided a new method for horizontal vibration reduction of the high-speed elevator car.

An Improved Deep Neural Network Model of Intelligent Vehicle Dynamics via Linear Decreasing Weight Particle Swarm and Invasive Weed Optimization Algorithms.

We propose an improved DNN modeling method based on two optimization algorithms, namely the linear decreasing weight particle swarm optimization (LDWPSO) algorithm and invasive weed optimization (IWO) algorithm, for predicting vehicle’s longitudinal-lateral responses. The proposed improved method can restrain the solutions of weight matrices and bias matrices from falling into a local optimum while training the DNN model. First, dynamic simulations for a vehicle are performed based on an efficient semirecursive multibody model for real-time data acquisition. Next, the vehicle data are processed and used to train and test the improved DNN model. The vehicle responses, which are obtained from the LDWPSO-DNN and IWO-DNN models, are compared with the DNN and multibody results. The comparative results show that the LDWPSO-DNN and IWO-DNN models predict accurate longitudinal-lateral responses in real-time without falling into a local optimum. The improved DNN model based on optimization algorithms can be employed for real-time simulation and preview control in intelligent vehicles.

Dynamic relationship network and international management of enterprise supply chain by particle swarm optimization algorithm under deep learning

AbstractThe traditional enterprise decision evaluation model based on neural network has the problems of mismatch with the optimal solution and slow convergence speed. In order to enable companies to make decisions that are in line with changes in the market, the particle swarm optimization (PSO) algorithm is used to optimize deep learning neural networks. Firstly, the model parameter setting is improved, and the inertia weight strategy of normal distribution attenuation is combined. On this basis, a normal distribution decay inertial weight particle swarm optimization (NDPSO) is proposed. The inertia weight of the optimized algorithm maintains a large value in the initial stage, which makes the PSO algorithm maintain a large step size in the optimization process and a small value in the later stage. Through experimental analysis, the trend parameter of the best normal distribution of the algorithm is obtained as 0.4433 and then using the detection function, the NDPSO algorithm is tested by two types of test functions. The NDPSO algorithm is compared with the optimization results of other algorithms which are optimized on the Sphere function. The minimum value of 554.29, the average value of 2032.11, and the standard deviation of 918.47, all of them are at the leading level. Taking into account other experimental results, it is proved that the normal distribution decay inertia weight can balance the global search and local development capabilities from the perspective of parameter improvement. It can speed up the convergence with ensuring the convergence accuracy. The improved PSO algorithm has certain optimization capabilities for neural network models. The use of optimized neural network models can enable companies to make decisions in line with changes in the market and optimize the dynamic relationship network of the company's supply chain, which is of great significance to the implementation of the company's international management.

Target Assignment Algorithm for Joint Air Defense Operation Based on Spatial Crowdsourcing Mode

Spatial crowdsourcing is a mode that uses distributed artificial computing power to solve specific function sets through Internet outsourcing. It has broad application value in the networked command and control of current joint air defense operations. In this paper, we introduce the spatial crowdsourcing theory into the field of target allocation for joint air defense operations and establish a weapon-target assignment model based on spatial crowdsourcing mode, which is more appropriate to the real situation and highlights the system cooperation capability of joint air defense operations. To solve the model, we propose a heuristic variable weight nonlinear learning factor particle swarm optimization (VWNF-PSO). This algorithm can significantly improve the efficiency and adaptability to weapon-target assignment problems under large-scale extreme conditions. Finally, we establish two kinds of joint air defense operation scenarios to verify the proposed model, then compare the proposed algorithm with variable weight PSO (VWPSO) and adaptive learning factor PSO (AFPSO), to validate the effectiveness and efficiency of the VWNF-PSO algorithm proposed in this paper.

Performance Optimization of Polarized MIMO Relay Channels Based on the COST 2100 Channel Model

Polarization selectivity has a great impact on multiple-input–multiple-output (MIMO) relay channels. In this letter, the channel capacity of MIMO relay channels is analyzed, revealing the polarization dependency of multipath components. Polarization optimization schemes are proposed, including a regular optimization and a dynamic weight particle swarm optimization (DWPSO) for MIMO relay channels. The performance analysis shows that the DWPSO can significantly improve the link capacity of MIMO relay channels based on the COST 2100 channel model. This study indicates that it is necessary to optimize the polarization direction of MIMO relay channels.

Logarithm Decreasing Inertia Weight Particle Swarm Optimization Algorithms for Convolutional Neural Network

The convolutional neural network (CNN) is a technique that is often used in deep learning. Various models have been proposed and improved for learning on CNN. When learning with CNN, it is important to determine the optimal parameters. This paper proposes an optimization of CNN parameters using logarithm decreasing inertia weight (LogDIW). This paper is used two datasets, i.e., MNIST and CIFAR-10 dataset. The MNIST learning experiment, the CIFAR-10 dataset, compared its accuracy with the CNN standard based on the LeNet-5 architectural model. When using the MNIST dataset, CNN's baseline was 94.02% at the 5th epoch, compared to CNN's LogDIWPSO, which improves accuracy. When using the CIFAR-10 dataset, the CNN baseline was 28.07% at the 10th epoch, compared to the LogDIWPSO CNN accuracy of 69.3%, which increased the accuracy.

Impacts of heuristic parameters in PSO inverse kinematics solvers

Abstract In this paper, an investigation is conducted in order to understand impacts of Particle Swarm Optimization (PSO) parameters on the convergence and the quality of the inverse kinematics solutions provided by the IK-PSO (inverse kinematics solver using PSO) – a heuristic inverse kinematics solver algorithm. Over a large panel of parameters investigations, a statistical proof of convergence is provided for 5 links to 60 links articulated system. A recommended set of parameters intervals are presented for this class of IK problems. Investigations are based on the standard inertia weight PSO, and concerned the impact of the inertia weight, the swarm size and the maximum iteration number. For a given set of parameters, the existence of a solution with a given position error is also proved. All tests were conducted over 100 times. The density of probability function, PDF, is used to approximate and analyze the fineness functions, which are the square of the position error. Results showed IK-PSO is an interesting IK solver when a set of good parameters are used. For these parameters, the algorithm showed a statistical proof of convergence with a high resolution, by mean of error position. The algorithm also showed time-effectiveness compared to CCD method, which is assumed to be a real-time IK heuristic solver used in gaming.

Adaptive weighted particle swarm optimization (AWPSO) attitude determination algorithm based on Chi-square test

The optimal search algorithm based on double-difference carrier phase is one of the methods to solve the attitude angle. An adaptive weighted particle swarm optimization (AWPSO) algorithm based on the Chi-square test is proposed to solve the attitude angle of ultra-short baseline. We establish the fitness function by introducing the relationship between attitude angle and baseline vector into the observation equations of double-difference carrier phase. Searching the attitude according to the fitness model instead of calculating the angle directly, which avoids solving the integer ambiguity. Using adaptive inertial weights and synchronous adaptive learning factors to speed up the convergence of attitude search. Constructing the candidate solution sequence to save the search result and calculating the Mahalanobis distance of the candidate solution. The local optimal solution is eliminated by the Chi-square test, and the attitude angle is gained by weighting the filtered sequence. Various static experimental results show that the new algorithm performs better than the direct solution method, the least square estimation method, and the PSO-based attitude solution method. The root mean square error of yaw angle and pitch angle is 0.245° and 0.236° directly at 0.575 m baseline.

Retraction: Study of Power Flow Optimisation for Distributed Energy Systems (J. Phys.: Conf. Ser. 2271 012013)

This article has been retracted by IOP Publishing following the recognition that the work contains accidental results through inaccurate methods.IOP Publishing has investigated and agrees the article contains inaccurate application of the Bayesian-Adaptive Weighted Particle Swarm Optimization algorithms in section 2.6 of the article. The purpose of Bayesian is to obtain the most appropriate values of them. Due to the authors lack of knowledge of Bayesian optimization, the accomplished steps in Table do not follow the principles well and form imprecise changes, resulting in accidental sets of parameters and therefore not supporting the description of the article. This casts serious doubt over the legitimacy of the article and/or expertise of the authors in this topic.It suggests the article’s results may be invalid as the original result cannot be obtained again.The author has agreed to this retraction. Retraction published: 13 December 2022

EBPSO: Enhanced binary particle swarm optimization for cardiac disease classification with feature selection

AbstractCardiac disease is one of the leading causes of death worldwide, and its early detection and diagnosis can considerably increase the lifespan of patients. An automated expert system for early and accurate diagnosis that offsets human error can be designed using machine intelligence and appropriate pre‐processing of data to ensure accuracy. To that end, enhanced binary particle swarm optimization (EBPSO) has been investigated in this paper to enable the definitive classification of cardiac disease with the aid of feature selection. The primary objective of this study is to improve the accuracy of the classification and the convergence speed while employing a lesser number of features. In order to achieve this, a new adaptive inertia weight was introduced to balance the exploitation and exploration capability of binary particle swarm optimization (BPSO). In addition, a novel idea called ‘neighbourhood best’ is introduced in the velocity update equation to improve the convergence speed. The proposed EBPSO approach was tested over well‐known datasets of heart disease, that is, the Cleveland dataset, Hungarian dataset, Switzerland dataset, and Long‐Beach‐Va datasets. For classification purposes, two well‐known classifiers, k‐nearest neighbour, and support vector machine were used with the EBPSO. Furthermore, the performance of EBPSO was compared with the traditional BPSO, as well as with BPSO with proposed inertia weight (Wa‐BPSO) and other PSO variants. It has been observed that the proposed approach EBPSO with KNN shows remarkable efficiency in terms of average classification accuracy and convergence rate. The average classification accuracy for Cleveland dataset is 92.814%, for the Hungarian dataset is 91.97%, for the Switzerland dataset is 91.626% with EBPSO+KNN and for the Long‐Beach‐Va dataset is 85.253% with EBPSO + SVM. Additionally, the performance of Wa‐BPSO was compared with other commonly used inertia weights. The results of this study demonstrated that the proposed approach produces better results in terms of both the accuracy of classification as well as the convergence rate with a reduced feature set.

Utilizing Logistic Map to Enhance the Population Diversity of PSO

Abstract Particle swarm optimization (PSO) is a high-quality, nature-inspired global optimization algorithm that can be applied to a variety of real-world optimization problems. PSO, on the other hand, has some flaws, such as slow convergence, premature convergence, and the ability to become stuck at local optimum solutions. This research aims to address the issue of population diversity in the PSO search process, which leads to premature convergence. As a result, in this study, a method is introduced to PSO in order to avoid early stagnation, which leads to premature convergence. A chaotic dynamic weight particle swarm optimization (CTPSOA) is proposed, in which a chaotic logistic map is delivered to increase the population range within the PSO search technique by editing the inertia weight of PSO to avoid premature convergence. This study also looks into the overall performance and viability of the proposed CTPSOA as a set of function selection rules for solving optimization issues. There are eight traditional benchmark functions that are used to assess the overall results and obtain the accuracy of the proposed (CTPSOA) algorithms when compared to a few other meta-heuristics optimization rules. The test results reveal that the CTPSOA outperforms other meta-heuristics algorithms in solving optimization problems by 85% and has established itself as a reliable and superior metaheuristics algorithm for feature selection.

Environmental/Economic Dispatch Using a New Hybridizing Algorithm Integrated with an Effective Constraint Handling Technique

This work tackles a relatively new issue in power system operation, known as the Environmental/Economic Dispatch problem. For this purpose, the combination of two powerful heuristic algorithms, namely, the Exchange Market Algorithm (EMA) and Adaptive Inertia Weight Particle Swarm Optimization (AIWPSO), was employed. Additionally, the Multiple Constraint Ranking (MCR) technique was used to address the system constraints such as prohibited operating zones and ramp rate limits. Furthermore, the mutation operator was used to improve the performance of the global search mechanism. The main purpose of combining these two algorithms was utilizing the EMA’s high performance to explore the global optimum and local exploitation ability of AIWPSO. The algorithm performance was evaluated on six standard benchmark functions and was scrutinized on several different test systems, including 6–40 units. By using the proposed method, the minimum values of the reduction in annual costs, with equal or less emissions, compared to other methods, were USD 17,520, 8760 and 10,801,080, respectively, for the 6-unit, 10-unit, and 40-unit test systems (assuming the same load profile throughout the year). Similarly, in the 14-unit test system for 1750, 2150, and 2650 (MW) load demands, these values were USD 229,879, 148,438, and 4483, respectively.

Early Fault Diagnosis of Gearbox Based on VWPSO-VMD

Abstract Aiming at the problem that the characteristic of the early weak fault of the gearbox is submerged by the background noise under the low-speed operating condition, this paper proposes a gearbox fault diagnosis method based on the variable weighted particle swarm optimization variational mode decomposition (VWPSO-VMD). First, a variable weight particle swarm optimization algorithm (VWPSO) is proposed, and the effectiveness of the VWPSO is verified by the optimization calculation of test functions. Secondly, using fuzzy entropy as the fitness function, the proposed VWPSO is used to optimize the modal number K and the penalty factor α of the variational mode decomposition (VMD). Then, based on the grey relational analysis method (GRA), the optimal intrinsic mode function (IMF) is selected for envelope demodulation analysis. Finally, VWPSO-VMD and empirical mode decomposition (EMD) are used to diagnose the early crack and broken tooth fault signals of the gearbox. The results show that, compared with EMD, VWPSO-VMD can more accurately extract the early fault characteristic frequency of the gearbox. This provides a new method for the extraction of early fault features of the gearbox.

Research on Low-Carbon Economic Operation Strategy of Renewable Energy-Pumped Storage Combined System

Aiming at the problem of insufficient peak shaving capacity of the power system after large-scale renewable energy such as wind power and photovoltaics is connected to the grid, a new energy-pumped storage combined system low-carbon economic operation strategy is proposed. First, the energy storage characteristics of pumped storage are studied; wind power and photovoltaic output are shifted in time and space; and a new energy-pumped storage combined system model with photovoltaics, wind power, and pumped storage as the main body is established. Second, the carbon trading mechanism was introduced, and the reward and punishment ladder carbon trading cost model was established. Then, via the new energy-pumped storage combined system, the upper optimization target with the smallest investment cost and the lower optimization target with the largest system benefit are established, and a two-layer optimization model solution method based on linearly decreasing inertia weight particle swarm optimization and sequential quadratic programming algorithm is proposed. Finally, via the operating data of a pumped storage power station on an island in northern China, the new energy-pumped storage combined system low-carbon economic operation strategy was simulated and verified. The simulation results show that the low-carbon economic operation strategy of the new energy-pumped storage combined system proposed in this paper can effectively suppress load fluctuations, reduce the impact of wind and wind anti-peak regulation characteristics on the power grid, increase the utilization rate of renewable energy, and improve system economy.

Study on fast speed fractional order gradient descent method and its application in neural networks

This article introduces a novel fractional order gradient descent method for the quadratic loss function. Based on Riemann-Liouville definition, a more practical fractional order gradient descent method with variable initial value is proposed to ensure convergence to the actual extremum. On this basis, the random weight particle swarm optimization algorithm is introduced to select the appropriate initial value, which not only accelerates the convergence speed, but also enhances the global convergence ability of the algorithm. To avoid complicated problems of the chain rule in fractional calculus, the parameters of output layers is trained by the new designed method, while the parameters of hidden layers still use the conventional method. By selecting proper hyper-parameters, the proposed method shows faster convergence speed than others. Finally, numerical examples are given to verify that the proposed algorithm has fast convergence speed and high accuracy under a adequate large number of independent runs.

Designing of High Voltage Cable Bonding with Intelligence Algorithms to Avoid Cable Insulation Faults and Electroshock in High Voltage Lines

The insulation fault is a major problem in high voltage cable lines. The major factors in the insulation faults are harmonic currents and the metal sheath voltage (MV) that occur on the metal sheath of cable. MV and harmonic distortion should be minimized to prevent insulation faults. Thus, sectional solid bonding with different grounding resistance (SSBr) method is developed as a new bonding method for minimizations of harmonic current and MV. Also, SSBr should be optimized by optimized according to minimum MV and harmonic distortion rate of high voltage cable. Inertia weighted particle swarm optimization (iPSO), particle swarm optimization (PSO), genetic algorithm (GA) and differential evolution algorithm (DEA) are used for optimization of SSBr, and three groups the prediction methods are used separately as objective function of the optimization methods to determine minimum MV and harmonic distortion. These groups are neural networks, hybrid neural networks and regression methods. Hybrid neural network with inertia weighted particle swarm optimization (H-iPSO), linear regression and feedforward backpropagation neural network are selected from their groups according to training errors. Solid bonding method is widely used for bonding of high voltage cable, and solid bonding is simulated in this study. When solid bonding is used for bonding of cable, maximum harmonic distortion rate is measured as 8.15 %, and maximum MV is measured as 1086 V. When H-iPSO is used as prediction method, and PSO is used as optimization method, maximum harmonic distortion rate is measured as 5,28 %, and maximum MV is measured as 57 V. Namely, both insulation fault and electroshock can be prevented by the optimized SSBr method.

Modified Adaptive Inertia Weight Particle Swarm Optimization for Data Clustering

Modified Adaptive Inertia Weight Particle Swarm Optimization for Data Clustering

Design of Highly Uniform Field Coils Based on the Magnetic Field Coupling Model and Improved PSO Algorithm in Atomic Sensors

The coupling between the coil and the cylindrical magnetic shield (CMS) in atomic sensors not only results in the inconsistency of the coil constant with the theory but also deteriorates the uniformity, which affects the sensor performance. To solve the above deficiencies, a design method of highly uniform field coils inside the CMS based on the coupling model with finite permeability and thickness and particle swarm optimization (PSO) with dynamic inertia weights is proposed for the first time. The coupling model of the coil inside the CMS is established by Green’s function method (GFM). Then it is combined with the nonlinear optimization method, and the coil configuration is optimized by the dynamic inertia weight PSO to obtain the optimal parameters. It not only reduces the calculation error of the model, but also improves the uniformity. Comparing this work with the Lee-Whiting coil inside a CMS, the theoretical uniformity is further improved by 2 orders of magnitude, and measured uniformity errors in the target region reach 1.404×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> , and 2.317×10 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-4</sup> , respectively. The correctness and effectiveness are proved by finite element simulation and experimental results. The accuracy and uniformity of coils can be considerably improved, which is beneficial to further improve the measurement sensitivity of instruments related to magnetic field uniformity.

Design of Optimal Backstepping Control for a Wind Power Plant System Using the Adaptive Weighted Particle Swarm Optimization

Design of Optimal Backstepping Control for a Wind Power Plant System Using the Adaptive Weighted Particle Swarm Optimization