Inertia Weight Particle Swarm Optimization Research Articles

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.

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.

Design of Legal Chinese English Simultaneous Interpretation System based on PSO Algorithm

Aiming at the phenomenon of “wrong words” and “missing words” in the process of Chinese English legal interpretation, a Chinese English legal simultaneous interpretation system based on PSO algorithm is designed. According to the construction requirements of fuzzy neural network, the optimization results of PSO inertia weight are determined, and then the system model optimization based on PSO algorithm is realized with the help of membership function. On this basis, this paper analyzes the key trigger factors of simultaneous interpretation, and distinguishes the specific differences between consecutive interpretation load and simultaneous interpretation by defining the way of legal Chinese English text transmission effect, so as to realize the smooth application of legal Chinese English simultaneous interpretation system based on PSO algorithm. The results shows that, compared with the consecutive interpretation system, the simultaneous interpretation system can effectively solve all the problems of “wrong words” and “missing words” in the process of legal Chinese English document translation, and effectively guarantee the authenticity of document samples.

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.

An overlapping peaks separation algorithm for ion mobility spectrometry based on second-order differentiation and dynamic inertia weight particle swarm optimization algorithm.

Ion mobility spectrometry (IMS) is a powerful analytical tool extensively applied in numerous domains. However, there still exists the phenomenon of peaks overlapping in the analysis of isomers with similar structures due to the limited resolution of IMS. In this paper, a dynamic inertia weight particle swarm optimization (DIWPSO) algorithm combined with second-order differentiation is proposed to separate the IMS overlapping peaks efficiently and precisely. It can identify the component number of overlapping peaks and limit those parameters (ion mobility, intensity, and full-width at half maximum of each single peak) of the peak model in a small range using second-order differentiation. Based on this, DIWPSO that has been set the best operating parameters is capable of accurately separating IMS overlapping peaks to identify the compound within a short time. A comparison between the performance of DIWPSO and the improved particle swarm optimization (IPSO) found that DIWPSO with separation errors less than 2.34% overall outperforms IPSO whose maximum error is up to 5.58%. Moreover, the running time of DIWPSO is 30-80 times less than that of IPSO, and DIWPSO exhibits stronger robustness. This method can automatically identify the component number of IMS overlapping peaks and resolve them with muticomponents and different overlapped degrees rapidly and accurately, which further improves the structural resolution of IMS.

Multi-Layer Hybrid Fuzzy Classification Based on SVM and Improved PSO for Speech Emotion Recognition

Speech Emotion Recognition (SER) plays a significant role in the field of Human–Computer Interaction (HCI) with a wide range of applications. However, there are still some issues in practical application. One of the issues is the difference between emotional expression amongst various individuals, and another is that some indistinguishable emotions may reduce the stability of the SER system. In this paper, we propose a multi-layer hybrid fuzzy support vector machine (MLHF-SVM) model, which includes three layers: feature extraction layer, pre-classification layer, and classification layer. The MLHF-SVM model solves the above-mentioned issues by fuzzy c-means (FCM) based on identification information of human and multi-layer SVM classifiers, respectively. In addition, to overcome the weakness that FCM tends to fall into local minima, an improved natural exponential inertia weight particle swarm optimization (IEPSO) algorithm is proposed and integrated with fuzzy c-means for optimization. Moreover, in the feature extraction layer, non-personalized features and personalized features are combined to improve accuracy. In order to verify the effectiveness of the proposed model, all emotions in three popular datasets are used for simulation. The results show that this model can effectively improve the success rate of classification and the maximum value of a single emotion recognition rate is 97.67% on the EmoDB dataset.

Gas diffusion model based on an improved Gaussian plume model for inverse calculations of the source strength

The rapid and accurate prediction of the release source and concentration of pollutants remains a crucial issue in emergency rescue. A suitable gas diffusion model, an appropriate distribution of monitoring points, and an inverse calculation method are required to solve this problem. Optimization modeling using monitoring data is proposed in this paper. The objective function is established using the sum of the squared errors between the observed and calculated concentrations. The Gaussian plume model was improved with ground reflection coefficient and modified He and compared with AFTOX as the monitoring data to increase the accuracy of the inverse calculation of the source strength. The stochastic inertia weight particle swarm optimization algorithm is utilized to meet the needs of emergency rescue operations for the more accurate prediction of the leakage point. The results show that it is necessary to establish a gas diffusion model for each location to ensure the accuracy of the source strength estimate.

Establishment of NOx Concentration Model at the Outlet of SCR Denitrification System for Alkali Recovery Furnace Flue Gas Based on Improved LSSVM

In the case of alkali recovery, it is difficult to measure with precision the NOx concentration at the output of the denitrification system. In order to solve this problem,variable adjustment experiments was carried out on the work site. According to the obtained modeling data, the least squares support vector machine (LSSVM) optimized by adaptive inertia weight particle swarm optimization (AIWPSO) was used to establish the NOx concentration model at the outlet of the SCR flue gas denitrification system in the alkali recovery section. Finally, comparing the model with the traditional mechanism model, the verification results show that the outlet NOx concentration model based on AIWPSO optimized LSSVM had the better fitting ability and error precision.

Linearly decreasing inertia weight PSO and improved weight factor-based clustering algorithm for wireless sensor networks

Linearly decreasing inertia weight PSO and improved weight factor-based clustering algorithm for wireless sensor networks

Dynamic soft sensor modeling method fusing process feature information based on an improved intelligent optimization algorithm

The lack of real-time and accurate melting index forecast model puts forward challenges to the online quality detection and control of the polypropylene industrial production. Meanwhile, the key process characteristics of dynamic and time delay are not fully and effectively integrated into the soft sensor model. Therefore, a novel dynamic soft sensing (DSS) modeling method using the orthogonal echo state network (OESN) based on the cumulative mutual information (CMI) threshold setting integrating an improved differential evolution algorithm (IDE) (OESN-CMI-IDE) is proposed. The mutual information of each candidate auxiliary variable relative to the dominant variable is calculated and sorted to determine the CMI threshold and select the corresponding auxiliary variable. Then an IDE algorithm is used to optimize the parameters of the OESN model, which has faster convergence, higher accuracy and stronger robustness than that of DE, self-adaptive DE (jDE), DE with time varying scale factor (DETVSF), line decreased inertia weight particle swarm optimization (LDPSO), flexible exponential inertia weight particle swarm optimization (FEPSO), and oscillating triangular inertia weight particle swarm optimization (OTPSO) in terms of evolutionary computing competition benchmark function tests. Finally, the proposed method is applied to predict the melt index in an actual polypropylene plant. The experimental results demonstrate that the prediction effect of the proposed method is better than that of the OESN model optimized by DE, jDE, DETVSF, LDPSO, FEPSO, OTPSO and the OESN model without optimization, which can escort the online monitoring and advanced control of the melt index in the propylene industry.

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

Highway Traffic Speed Prediction in Rainy Environment Based on APSO-GRU

In order to accurately analyse the impact of the rainy environment on the characteristics of highway traffic flow, a short-term traffic flow speed prediction model based on gate recurrent unit (GRU) and adaptive nonlinear inertia weight particle swarm optimization (APSO) was proposed. Firstly, the rainfall and highway traffic flow data were cleaned, and then they are matched according to the spatiotemporal relationship. Secondly, through the method of multivariate analysis of variance, the significance of the impact of potential factors on traffic flow speed was explored. Then, a GRU-based traffic flow speed prediction model in rainy environment is proposed, and the actual road sections under different rainfall scenarios were verified. After that, in view of the problem that the prediction accuracy of the GRU model was low in the continuous rainfall scenario, the APSO algorithm was used to optimize the parameters of the GRU network, and the APSO-GRU prediction model was constructed and verifications under the same road section and rain scene were carried out. The results show that the APSO-GRU model has significantly improved prediction stability than the GRU model and can better extract rainfall features during continuous rainfall, with an average prediction accuracy rate of 96.74%.

Clustering and classification with inertia weight and elitism-based particle swarm optimization

Clustering and classification with inertia weight and elitism-based particle swarm optimization

Solving Economic Load Dispatch Problem UsingParticle Swarm Optimization Technique

Economic load dispatch (ELD)is one of the important problems ofpower system operation. Conventional methods like Lambda iteration methodare not efficientfor complex ELD problems. Particle swarm optimization is preferred in ELD problem due to its high performance.The Inertia Weight PSO and Constriction Factor PSO algorithms are performed on threeunit and sixunit systems. The analysis of ELD problem is performed by Conventional method and PSO method. In this paper,losses are neglected in the ELD problem. PSO algorithm obtains the best solution forELD problem.

Transmission congestion management considering voltage stability margin

This paper presents a solution for the transmission congestion management considering voltage stability issues using optimal generation rescheduling. While practicing congestion management using optimization techniques, the control variables remain under their upper or lower limits but it may lead to the lowered level of voltage security after optimization. To counterbalance this adverse effect, a modified objective function has been used. The reactive power generation rescheduling and reactive support from capacitors have been incorporated along with active power generation rescheduling to manage congestion as well as to improve the network voltage stability margin. The Random Inertia Weight Particle Swarm Optimization (RANDIW-PSO) algorithm has been employed in this paper to obtain optimized solutions. The proposed methodology is tested on the New-England test system for different realistic scenarios. The results confirm a noteworthy decline in congestion cost along with the improvement in network voltage stability margin. Moreover, system performance has been improved in terms of system power losses, increased reactive power reserve at generators and voltage profile.

Improved Metaheuristic Optimization Algorithm Applied to Hydrogen Fuel Cell and Photovoltaic Cell Parameter Extraction

As carriers of green energy, proton exchange membrane fuel cells (PEMFCs) and photovoltaic (PV) cells are complex and nonlinear multivariate systems. For simulation analysis, optimization control, efficacy prediction, and fault diagnosis, it is crucial to rapidly and accurately establish reliability modules and extract parameters from the system modules. This study employed three types of particle swarm optimization (PSO) algorithms to find the optimal parameters of two energy models by minimizing the sum squared errors (SSE) and roots mean squared errors (RMSE). The three algorithms are inertia weight PSO, constriction PSO, and momentum PSO. The obtained calculation results of these three algorithms were compared with those obtained using algorithms from other relevant studies. This study revealed that the use of momentum PSO enables rapid convergence (under 30 convergence times) and the most accurate modeling and yields the most stable parameter extraction (SSE of PEMFC is 2.0656, RMSE of PV cells is 8.839 · 10−4). In summary, momentum PSO is the algorithm that is most suitable for system parameter identification with multiple dimensions and complex modules.

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.

Hardware in the Loop Testing of Adaptive Inertia Weight PSO-Tuned LQR Applied to Vehicle Suspension Control

This paper presents an adaptive inertia weight particle swarm optimization (AIWPSO) employed for solving the multiobjective weight optimization problem of LQR applied for the vehicle active suspension system (ASS). To meet the competing control objectives of ASS including the ride comfort, road handling, and suspension travel, the state feedback controller design for ASS is formulated as an optimization problem and an improved PSO is employed for finding the optimal weights of the linear-quadratic regulator (LQR). Specifically, for solving the premature convergence of the particles and imbalance between exploration and exploitation capabilities of PSO, an adaptive inertia weight that updates the velocity of the particles based on the success rate is used. The efficacy of the AIWPSO-tuned LQR is experimentally tested on a quarter-car ASS plant using the hardware in loop (HIL) testing for an uneven road surface. Experimental results highlight that, compared to conventional PSO-tuned LQR, the proposed scheme can significantly minimize the vehicle body acceleration due to irregular road profile while guaranteeing the minimum tire friction for passenger safety. The ISO 2361-1 standards adopted to evaluate the ride and health criteria substantiate that the proposed scheme reduces the vibration dose value by 25.34% for a bumpy road profile. Moreover, the cumulative power spectral density (CPSD) of vehicle body acceleration assessed in both low- and high-frequency regions manifests the significant improvement in the ride comfort.