Decreasing Inertia Weight Particle Swarm Research Articles

An Improved Particle Swarm Optimization Algorithm for Automated Test Resource Allocation

With the rapid increase of software complexity, automated testing has become the mainstream of software testing. However, the current automated test tools adopt a large number of common test equipment resource. At the beginning of the test, for specific test scenarios, the testers need to plan the test equipment resources according to the requirements of the software under test. An automatic test equipment resource planning and optimization algorithm based on group optimization is proposed IPSO (Improved Particle Swarm Optimization) to solve this problem. This method maps the requirement description to the equipment description to obtain the matching matrix, and then establishes a multi-objective constrained optimization model for test equipment resource planning. The solution is obtained through improved particle swarm optimization algorithm to achieve the optimal planning of test equipment resources. Compared with traditional manual planning methods, this algorithm reduces the workload of testers, and improves the automation and efficiency of testing. Experimental results demonstrate that the optimal solutions found by the algorithm proposed in this paper is superior to those of the PSO (Particle Swarm Optimization)) and Linearly Decreasing Inertia Weight Particle Swarm Optimization (LWPSO) algorithms.

How to realize the power demand side actively matching the supply side? ——A virtual real-time electricity prices optimization model based on credit mechanism

Intermittence and fluctuation of renewable energy increase the cost of real-time balance of power supply and demand, which will affect the stability of the power grid. How to design the incentive mechanism to increase the flexibility of the distributed energy system and reduce the cost of real-time supply–demand balance is an urgent problem to be solved. In this study, from the perspective of the demand side actively matching the supply side, a virtual real-time electricity price optimization model based on the credit mechanism is proposed. We use the linear decreasing inertia weight particle swarm optimization algorithm to optimize the user's electricity price and realize the minimization of the total electricity cost. The influence of the virtual real-time electricity price incentive mechanism on the matching degree between the power load curve and the distributed energy output curve is also studied. Moreover, a novel index is put forward to quantitatively measure the matching degree between users’ power load curve and distributed energy output curve. Taking a multi-microgrid system in a China’s industrial park as the case, we have found the results that the virtual real-time electricity price optimization model can not only reduce users’ total power cost by 7.8108%, but also improve the matching degree between the demand side and the supply side by 2.4643%. The new demand response incentive mechanism we have presented in this paper may promote the effective utilization of distributed energy and reduce the impact of renewable energy on power grid.

Analysis and improvement of the uniformity of magnetic field coil based on the cylindrical magnetic shield in atomic magnetometers

The magnetic field performance of the uniform coils will be affected by magnetic shields and produce errors in the atomic magnetometer. It is necessary to analyze the influencing factors and guide the coil design. Aiming at the above shortcomings, this paper analyzes the coupling effect of magnetic shield and coil and proposed a Lee-Whiting coil design method based on the linearly decreasing inertia weight particle swarm optimization (LDW-PSO) algorithm. Firstly, the magnetic field model under the ferromagnetic boundary is established, and the effects of the size, thickness and permeability of the magnetic shield on the magnitude and uniformity of the Lee-Whiting coil magnetic field are analyzed. The results show that the coupling coefficient of the coil changes monotonically with the change of the size of the magnetic shield, while the uniformity error has a minimum value with the change of the size of the shield. In addition, an improved Lee-Whiting coil is designed by combining the LDW-PSO algorithm, which improves the uniformity of the coil. Compared with the original coil, the measured uniformity error of the improved coil is reduced from 8.39 × 1 0 − 4 to 2.81 × 1 0 − 4 . The accuracy of the theoretical analysis is verified by experiments. This paper is of great significance to study the coupling and optimization design of the coil based on the magnetic shields in atomic magnetometers. • The influence of magnetic shield parameters on coil uniformity is analyzed. • The optimal coil size for the uniformity in magnetic shield is given by analysis and testing. • The improved Lee-Whiting coil is designed by intelligent optimization algorithm. • Improved Lee-Whiting coil overcomes the coupling effect between coil and magnetic shield. • The correctness of the analysis and the effectiveness of the design are proved by experiments.

Double-layer Optimization Study of Virtual Power Plants with Source-load-storage

In order to effectively solve the supply-demand imbalance of the grid caused by intermittent new energy penetration and improve the scheduling flexibility of virtual power plants, a two-layer optimization model of virtual power plants with source-load-storage is proposed. The upper layer of the model is designed to reduce the forecast error of wind power output and improve the revenue of the virtual power plant. On the basis of the maximum wind power consumption, distributed energy storage and fuel cells are used to coordinate and compensate for the deviation between the planned and actual output of renewable energy. The active output of the upper layer is transferred to the lower layer. The lower layer of the model is solved by using the improved linear decreasing inertia weight particle swarm algorithm for the output of the gas turbine unit. The analysis results show that the model can absorb the excess new energy generation in the grid, achieve the balance of supply and demand regulation in the grid, and effectively reduce the economic operation cost of the system.

Multilevel Thresholding Image Segmentation Based-Logarithm Decreasing Inertia Weight Particle Swarm Optimization

Abstract The image segmentatation technique that is often used is thresholding. Image segmentation is a process of dividing the image into different regions according to their similar characteristics. This research proposes a multilevel thresholding algorithm using modified particle swarm optimization to solve a segmentation problem. The threshold optimal values are determined by maximizing Otsu’s objective function using optimization technique namely particle swarm optimization based on the logarithmic decreasing inertia weight (LogDIWPSO). The proposed method reduces the computational time to find the optimum thresholds of multilevel thresholding which evaluated on several grayscale images. A detailed comparison analysis with other multilevel thresholding based techniques namely particle swarm optimization (PSO), iterative particle swarm optimization (IPSO), and genetic algorithms (GA), From the experiments, Modified particle swarm optimization (MoPSO) produces better performance compared to the other methods in terms of fitness value, robustness and convergence. Therefore, it can be concluded that MoPSO is a good approach in finding the optimal threshold value. Keywords: grayscale image, inertia weight, image segmentation, particle swarm optimization.

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.

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.

An Investigation of Logarithm Decreasing Inertia Weight Particle Swarm Optimization in Global Optimization Problem

An Investigation of Logarithm Decreasing Inertia Weight Particle Swarm Optimization in Global Optimization Problem

Prediction and optimization of isentropic efficiency of vortex pump under full operating conditions in Organic Rankine Cycle waste heat recovery system based on deep learning and intelligent algorithm

The isentropic efficiency of vortex pump in Organic Rankine Cycle (ORC) system has an important influence on the performance of the system. In this paper, vortex pump experimental data and deep learning are combined to construct an experimental data-driven isentropic efficiency prediction model of vortex pump under full operating conditions. The activation function and the number of hidden layer nodes in the model are filtered by nested screening technique. Through bilinear interpolation algorithm, the influence of vortex pump operation parameters on the isentropic efficiency is analyzed. In addition, the optimization boundary is selected in the four-dimensional space. Finally, the deep learning prediction model is combined with Linear Decreasing Inertia Weight Particle Swarm Optimization (LDIWPSO) to predict and optimize the maximum isentropic efficiency of vortex pump under full operating conditions. Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE) and Coefficient of Determination (R-square) are combined to evaluate the prediction accuracy of the model. The prediction and optimization results show that the maximum isentropic efficiency of vortex pump can reach 22.89%. The combination of deep learning and LDIWPSO can predict and optimize the maximum isentropic efficiency for vortex pump with high precision. It also provides a reference for the maximum value of vortex pump isentropic efficiency in theoretical analysis and numerical simulation.

Special issue on applications and techniques in cyber intelligence 2019

This special issue focuses on all aspects on techniques and applications in cyber intelligence research. The purpose of this special issue is to provide a forum for presentation and discussion of innovative ideas, cutting edge research results, and novel techniques, methods and applications on all aspects of cyber intelligence. It includes selected articles from 2019 International Conference on Applications and Techniques in Cyber Intelligence (ATCI2019) June 22-24, 2019 Huainan, China as well as an open call. The submitted articles were reviewed by experts from both academia and industry. After two rounds of reviewing, the 15 highest quality manuscripts were suggested for EiC to accept for this special issue. This special issue will be published by CCPE as special issues. A combination weighted clustering algorithm is proposed by Li et al1 in the study to reduce the communication overhead of the distributed cognitive network and maintain the stability of the network structure. Li2 investigates edge-forwarding index of routing in the lexicographic product network, which is used to measure the congestion of an edge. Shang et al3 presents the on-chip evolution system of combinational logic circuits by a new hybrid algorithm known as improved genetic-simulated annealing algorithm. Jianmin et al4 proposes the uniform distribution code, model identification code, standard dimension code, and attribute dimensional code. Aiming at the actual production needs of the Dadingzi mountain hydrojunction, An et al5 studies the optimal control of the flood discharge system. Xu et al6 attempts to analyze the traffic network from the perspective of vulnerability. Ding et al7 provides a basis for adjusting the transport organization plan and evacuating passenger flow in emergency. In order to solve the problems of low utilization of the shipyard unreasonable placed of the blocks and unbalanced use of flat transporters, grid search algorithm for block storage yard scheduling is proposed by Zhou et al.8 Pei et al9 establishes a customer experience evaluation index system from the perspectives of O2O takeout platform, delivery staff, and distribution process. To realize the benign interaction between electric vehicle charging and switching power stations and power grid, and the demand-side management of electric vehicle charging and switching power stations, the Monte Carlo random sampling method is used by Gan and Zheng10 to collect the battery-charged state of batteries in one day. Yang and Liu11 draws on the theory of biological immune system, combines the ontology with the growth risk of start-ups, and uses SWRL and Jess engine to build an ontology knowledge base of start-ups' growth risk to identify various risks in the growth process of start-up. Tran et al12 provides a comprehensive research on cellular network traffic prediction using exponential smoothing methods. Guo and Chen13 proposes an interval support vector domain description method through using the dynamic decreasing inertia weight particle swarm optimization. Xu et al14 present the development of a cloud-based difference detection method using big GPR data for roadbed damage detection. Lu et al15 focuses on textual cyberbullying detection because text is the most common form of social media. The guest editors would like to thank Prof. Geoffrey C. Fox who is the editor in chief of CCPE. His help and trust is the most important thing for the success of this SI. The guest editors would like to thank the reviewers for their high-quality reviews, which provided insightful and constructive feedback to the authors of the articles.

An interval support vector domain description based on the dynamic decreasing inertia weight particle swarm optimization

SummaryFor the fault diagnosis of the interval rotor crack fault samples, this paper proposes an interval support vector domain description method through using the dynamic decreasing inertia weight particle swarm optimization (DDIWPSO). Firstly, the interval Gauss function is constructed by using the interval midpoint and interval radius. Applying the interval Gauss kernel function as the kernel, the interval support vector domain description is proposed and can realize the classification of interval samples. Secondly, the DDIWPSO is applied to select the optimal penalty parameter C, the Gauss interval kernel width parameter σ, and the factor λ of the proposed method. Finally, interval University of California Irvine (UCI) samples and the interval rotor crack data are used to verify the advantages of this method. The experimental verification shows that the interval support vector domain description method has the higher accuracy compared with the traditional interval fault classification methods because this method owns the empirical risk minimization of SVM and the optimal parameters selection based on the DDIWPSO.

Fault diagnosis method for rolling bearings based on the interval support vector domain description

Aiming at the fault classification problem of the rolling bearing under the uncertain structure parameters work condition, this paper proposes a fault diagnosis method based on the interval support vector domain description (ISVDD). Firstly, intrinsic time scale decomposition is performed for vibration signals of the rolling bearing to get the time-frequency spectrum samples. These samples are divided into a training set and a test set. Then, the training set is used to train the ISVDD. Meanwhile, the dynamic decreasing inertia weight particle swarm optimization is applied to improve the training accuracy of ISVDD model. Finally, the performance of the four interval classifiers is calculated in rolling bearing fault test set. The experimental results show the advantages of the ISVDD model: (1) ISVDD can extend the support vector domain description to solve the uncertain interval rolling bearing fault classification problem effectively; (2) The proposed ISVDD has the highest classification accuracy in four interval classification methods for the different rolling bearing fault types.

Prediction of Military Vehicle's Drawbar Pull Based on an Improved Relevance Vector Machine and Real Vehicle Tests.

The scientific and effective prediction of drawbar pull is of great importance in the evaluation of military vehicle trafficability. Nevertheless, the existing prediction models have demonstrated lots of inherent limitations. In this framework, a multiple-kernel relevance vector machine model (MkRVM) including Gaussian kernel and polynomial kernel is proposed to predict drawbar pull. Nonlinear decreasing inertia weight particle swarm optimization (NDIWPSO) is employed for parameter optimization. As the relations between drawbar pull and its influencing factors have not been tested on real vehicles, a series of experimental analyses based on real vehicle test data are done to confirm the effective influencing factors. A dynamic testing system is applied to conduct field tests and gain required test data. Gaussian kernel RVM, polynomial kernel RVM, support vector machine (SVM) and generalized regression neural network (GRNN) are also used to compare with the MkRVM model. The results indicate that the MkRVM model is a preferable model in this case. Finally, the proposed novel model is compared to the traditional prediction model of drawbar pull. The results show that the MkRVM model significantly improves the prediction accuracy. A great potential of improved RVM is indicated in further research of wheel-soil interactions.

Optimization of Liquid Desiccant Regenerator with Multiobject Particle Swarm Optimization Algorithm

In this paper, a model-based optimization strategy for a liquid desiccant regenerator operating with lithium chloride solution is presented. By analyzing the characteristics of the components, such as electric heater, pump, and fan, energy predictive models for the components in the regenerator are developed. To minimize the energy usage while maintaining the regeneration rate within an accepted level, one multiobjective optimization problem is formulated with two objectives, the constraints of decision variables, components interactions, and the outdoor conditions. A multiobjective optimization strategy based on decreasing inertia weight particle swarm optimization (DIWPSO) is proposed to obtain the optimal nondominated solutions of the optimization problem, and a decision making strategy is introduced to select the final solution, desiccant solution flow rate, desiccant solution temperature, and the regenerating air flow rate, to minimize the energy usage in the regenerator. Experimental studies are carried out on an existing system to compare the energy consumption and regeneration rate between the proposed optimization strategy and conventional strategy to evaluate energy saving performance of the proposed strategy. Experimental results demonstrate that an average of 8.55% energy can be saved by implementing the proposed optimization strategy in liquid desiccant regenerator.

Novel Particle Swarm Optimization and Its Application in Calibrating the Underwater Transponder Coordinates

A novel improved particle swarm algorithm named competition particle swarm optimization (CPSO) is proposed to calibrate the Underwater Transponder coordinates. To improve the performance of the algorithm, TVAC algorithm is introduced into CPSO to present anextension competition particle swarm optimization(ECPSO). The proposed method is tested with a set of 10 standard optimization benchmark problems and the results are compared with those obtained through existing PSO algorithms,basic particle swarm optimization(BPSO),linear decreasing inertia weight particle swarm optimization(LWPSO),exponential inertia weight particle swarm optimization(EPSO), andtime-varying acceleration coefficient(TVAC). The results demonstrate that CPSO and ECPSO manifest faster searching speed, accuracy, and stability. The searching performance for multimodulus function of ECPSO is superior to CPSO. At last, calibration of the underwater transponder coordinates is present using particle swarm algorithm, and novel improved particle swarm algorithm shows better performance than other algorithms.

Radial Basis Function Neural Network Based on an Improved Exponential Decreasing Inertia Weight-Particle Swarm Optimization Algorithm for AQI Prediction

This paper proposed a novel radial basis function (RBF) neural network model optimized by exponential decreasing inertia weight particle swarm optimization (EDIW-PSO). Based on the inertia weight decreasing strategy, we propose a new Exponential Decreasing Inertia Weight (EDIW) to improve the PSO algorithm. We use the modified EDIW-PSO algorithm to determine the centers, widths, and connection weights of RBF neural network. To assess the performance of the proposed EDIW-PSO-RBF model, we choose the daily air quality index (AQI) of Xi’an for prediction and obtain improved results.

Analog Circuit Fault Diagnosis Based on Particle Swarm Neural Network Hybrid Algorithm

Base on improved particle swarm algorithm, this paper proposes a linear decreasing inertia weight particle swarm algorithm and error back propagation algorithm based on hybrid algorithm combining. The linear decreasing inertia weight particle swarm algorithm and momentum-adaptive learning rate BP algorithm interchangeably adjust the network weights, so that the two algorithms are complementary. It gives full play to the PSO's global optimization ability and the BP algorithm local search advantage, to overcome the slow convergence speed and easily falling into local weight problems. Simulation results show that this diagnostic method can be used for tolerance analog circuit fault diagnosis, with a high convergence rate and diagnostic accuracy.

On the Performance of Linear Decreasing Inertia Weight Particle Swarm Optimization for Global Optimization

Linear decreasing inertia weight (LDIW) strategy was introduced to improve on the performance of the original particle swarm optimization (PSO). However, linear decreasing inertia weight PSO (LDIW-PSO) algorithm is known to have the shortcoming of premature convergence in solving complex (multipeak) optimization problems due to lack of enough momentum for particles to do exploitation as the algorithm approaches its terminal point. Researchers have tried to address this shortcoming by modifying LDIW-PSO or proposing new PSO variants. Some of these variants have been claimed to outperform LDIW-PSO. The major goal of this paper is to experimentally establish the fact that LDIW-PSO is very much efficient if its parameters are properly set. First, an experiment was conducted to acquire a percentage value of the search space limits to compute the particle velocity limits in LDIW-PSO based on commonly used benchmark global optimization problems. Second, using the experimentally obtained values, five well-known benchmark optimization problems were used to show the outstanding performance of LDIW-PSO over some of its competitors which have in the past claimed superiority over it. Two other recent PSO variants with different inertia weight strategies were also compared with LDIW-PSO with the latter outperforming both in the simulation experiments conducted.

Hybrid Optimization of Emission and Economic Dispatch by the Sigmoid Decreasing Inertia Weight Particle Swarm Optimization

Hybrid Optimization of Emission and Economic Dispatch by the Sigmoid Decreasing Inertia Weight Particle Swarm Optimization