A PSO algorithm-based seasonal nonlinear grey Bernoulli model with fractional order accumulation for forecasting quarterly hydropower generation

2021 ◽  
Vol 40 (1) ◽  
pp. 507-519
Author(s):  
Jianming Jiang ◽  
Wen-Ze Wu ◽  
Qi Li ◽  
Yu Zhang
Keyword(s):  
Electricity Generation ◽  
Moving Average ◽  
Pso Algorithm ◽  
Bernoulli Model ◽  
Hydropower Generation ◽  
Seasonal Fluctuations ◽  
Swarm Optimization ◽  
Electricity System ◽  
Proposed Model ◽  
Seasonal Factor

The hydropower plays a key role in electricity system owing to its renewability and largest share of clean electricity generation that promotes sustainable development of national economy. Developing a proper forecasting model for the quarterly hydropower generation is crucial for associated energy sectors, which could assist policymakers in adjusting corresponding schemes for facing with sustained demands. For this purpose, this paper presents a fractional nonlinear grey Bernoulli model (abbreviated as FANGBM(1,1)) coupled seasonal factor and Particular Swarm Optimization (PSO) algorithm, namely PSO algorithm-based FASNGBM(1,1) model. In the proposed method, the moving average method that eliminates the seasonal fluctuations is introduced into FANGBM(1,1), then in which the structure parameters of FASNGBM(1,1) are determined by PSO. Based on hydropower generation of China from the first quarter of 2011 to the final quarter of 2018 (2011Q1-2018Q4), the numerical results show that the proposed model has a better performance than that of other benchmark models. Eventually, the quarterly hydropower generation of China from 2019 to 2020 are forecasted by the proposed model, according to results, the hydropower generation of China will reach 11287.14 × 108 Kwh in 2020.

Adaptive particle swarm optimization algorithm based long short-term memory networks for sentiment analysis

2021 ◽  
pp. 1-17
Author(s):  
J. Shobana ◽  
M. Murali
Keyword(s):  
Particle Swarm Optimization ◽  
Sentiment Analysis ◽  
Language Processing ◽  
Short Term Memory ◽  
Contextual Information ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Adaptive Particle Swarm Optimization ◽  
Proposed Model

Text Sentiment analysis is the process of predicting whether a segment of text has opinionated or objective content and analyzing the polarity of the text’s sentiment. Understanding the needs and behavior of the target customer plays a vital role in the success of the business so the sentiment analysis process would help the marketer to improve the quality of the product as well as a shopper to buy the correct product. Due to its automatic learning capability, deep learning is the current research interest in Natural language processing. Skip-gram architecture is used in the proposed model for better extraction of the semantic relationships as well as contextual information of words. However, the main contribution of this work is Adaptive Particle Swarm Optimization (APSO) algorithm based LSTM for sentiment analysis. LSTM is used in the proposed model for understanding complex patterns in textual data. To improve the performance of the LSTM, weight parameters are enhanced by presenting the Adaptive PSO algorithm. Opposition based learning (OBL) method combined with PSO algorithm becomes the Adaptive Particle Swarm Optimization (APSO) classifier which assists LSTM in selecting optimal weight for the environment in less number of iterations. So APSO - LSTM ‘s ability in adjusting the attributes such as optimal weights and learning rates combined with the good hyper parameter choices leads to improved accuracy and reduces losses. Extensive experiments were conducted on four datasets proved that our proposed APSO-LSTM model secured higher accuracy over the classical methods such as traditional LSTM, ANN, and SVM. According to simulation results, the proposed model is outperforming other existing models.

scholarly journals Novel Ensemble Approach of Deep Learning Neural Network (DLNN) Model and Particle Swarm Optimization (PSO) Algorithm for Prediction of Gully Erosion Susceptibility

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5609 ◽  
Author(s):  
Shahab S. Band ◽  
Saeid Janizadeh ◽  
Subodh Chandra Pal ◽  
Asish Saha ◽  
Rabin Chakrabortty ◽  
...  
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Deep Learning ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Gully Erosion ◽  
Slope Aspect ◽  
Swarm Optimization ◽  
Proposed Model ◽  
Deep Learning Neural Network

This study aims to evaluate a new approach in modeling gully erosion susceptibility (GES) based on a deep learning neural network (DLNN) model and an ensemble particle swarm optimization (PSO) algorithm with DLNN (PSO-DLNN), comparing these approaches with common artificial neural network (ANN) and support vector machine (SVM) models in Shirahan watershed, Iran. For this purpose, 13 independent variables affecting GES in the study area, namely, altitude, slope, aspect, plan curvature, profile curvature, drainage density, distance from a river, land use, soil, lithology, rainfall, stream power index (SPI), and topographic wetness index (TWI), were prepared. A total of 132 gully erosion locations were identified during field visits. To implement the proposed model, the dataset was divided into the two categories of training (70%) and testing (30%). The results indicate that the area under the curve (AUC) value from receiver operating characteristic (ROC) considering the testing datasets of PSO-DLNN is 0.89, which indicates superb accuracy. The rest of the models are associated with optimal accuracy and have similar results to the PSO-DLNN model; the AUC values from ROC of DLNN, SVM, and ANN for the testing datasets are 0.87, 0.85, and 0.84, respectively. The efficiency of the proposed model in terms of prediction of GES was increased. Therefore, it can be concluded that the DLNN model and its ensemble with the PSO algorithm can be used as a novel and practical method to predict gully erosion susceptibility, which can help planners and managers to manage and reduce the risk of this phenomenon.

scholarly journals Traffic Flow Anomaly Detection Based on Robust Ridge Regression with Particle Swarm Optimization Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-10
Author(s):  
Mingzhu Tang ◽  
Xiangwan Fu ◽  
Huawei Wu ◽  
Qi Huang ◽  
Qi Zhao
Keyword(s):  
Anomaly Detection ◽  
Traffic Flow ◽  
Ridge Regression ◽  
Cross Validation ◽  
Sliding Window ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Feature Sets ◽  
Proposed Model ◽  
Fold Cross Validation

Traffic flow anomaly detection is helpful to improve the efficiency and reliability of detecting fault behavior and the overall effectiveness of the traffic operation. The data detected by the traffic flow sensor contains a lot of noise due to equipment failure, environmental interference, and other factors. In the case of large traffic flow data noises, a traffic flow anomaly detection method based on robust ridge regression with particle swarm optimization (PSO) algorithm is proposed. Feature sets containing historical characteristics with a strong linear correlation and statistical characteristics using the optimal sliding window are constructed. Then by providing the feature sets inputs to the PSO-Huber-Ridge model and the model outputs the traffic flow. The Huber loss function is recommended to reduce noise interference in the traffic flow. The L2 regular term of the ridge regression is employed to reduce the degree of overfitting of the model training. A fitness function is constructed, which can balance the relative size between the k-fold cross-validation root mean square error and the k-fold cross-validation average absolute error with the control parameter η to improve the optimization efficiency of the optimization algorithm and the generalization ability of the proposed model. The hyperparameters of the robust ridge regression forecast model are optimized by the PSO algorithm to obtain the optimal hyperparameters. The traffic flow data set is used to train and validate the proposed model. Compared with other optimization methods, the proposed model has the lowest RMSE, MAE, and MAPE. Finally, the traffic flow that forecasted by the proposed model is used to perform anomaly detection. The abnormality of the error between the forecasted value and the actual value is detected by the abnormal traffic flow threshold based on the sliding window. The experimental results verify the validity of the proposed anomaly detection model.

An Adaptive Chaos Quantum-Behaved Particle Swarm Optimization Algorithm for Real-Time Dispatch in Power System with Wind Generation

2014 ◽  
Vol 1070-1072 ◽  
pp. 297-302
Author(s):  
Zhi Kui Wu ◽  
Chang Hong Deng ◽  
Yong Xiao ◽  
Wei Xing Zhao ◽  
Qiu Shi Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Real Time ◽  
Wind Power ◽  
Wind Farm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Good Convergence ◽  
Proposed Model

A real-time dispatch (RTD) model for wind power incorporated power system aimed at maximizing wind power utilization and minimizing fuel cost is proposed in this paper. To cope with the prematurity and local convergence of conventional particle swarm optimization (PSO) algorithm, a novel adaptive chaos quantum-behaved particle swarm optimization (ACQPSO) algorithm is put forward. The adaptive inertia weight and chaotic perturbation mechanism are employed to improve the particle’s search efficiency. Numerical simulation on a 10 unit system with a wind farm demonstrates that the proposed model can maximize wind power utilization while ensuring the safe and economic operation of the power system. The proposed ACQPSO algorithm is of good convergence quality and the computation speed can meet the requirement of RTD.

Evolutionary Algorithms For k-Anonymity In Social Networks Based On Clustering Approach

2019 ◽  
Vol 63 (7) ◽  
pp. 1039-1062 ◽  
Author(s):  
Navid Yazdanjue ◽  
Mohammad Fathian ◽  
Babak Amiri
Keyword(s):  
Social Networks ◽  
Convergence Rate ◽  
Structural Information ◽  
Main Idea ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Proposed Model ◽  
High Convergence Rate ◽  
Hybrid Solutions ◽  
Clustering Approach

AbstractThe usage of social networks shows a growing trend in recent years. Due to a large number of online social networking users, there is a lot of data within these networks. Recently, advances in technology have made it possible to extract useful information about individuals and the interactions among them. In parallel, several methods and techniques were proposed to preserve the users’ privacy through the anonymization of social network graphs. In this regard, the utilization of the k-anonymity method, where k is the required threshold of structural anonymity, is among the most useful techniques. In this technique, the nodes are clustered together to form the super-nodes of size at least k. Our main idea in this paper is, initially, to optimize the clustering process in the k-anonymity method by means of the particle swarm optimization (PSO) algorithm in order to minimize the normalized structural information loss (NSIL), which is equal to maximizing 1-NSIL. Although the proposed PSO-based method shows a higher convergence rate than the previously introduced genetic algorithm (GA) method, it did not provide a lower NSIL value. Therefore, in order to achieve the NSIL value provided by GA optimization while preserving the high convergence rate obtained from the PSO algorithm, we present hybrid solutions based on the GA and PSO algorithms. Eventually, in order to achieve indistinguishable nodes, the edge generalization process is employed based on their relationships. The simulation results demonstrate the efficiency of the proposed model to balance the maximized 1-NSIL and the algorithm’s convergence rate.

scholarly journals Modeling COVID-19 in Iran using Particle Swarm Optimization algorithm

2021 ◽  
Author(s):  
Ebrahim Sahafizadeh ◽  
MohammadAli Khajeian
Keyword(s):  
Particle Swarm Optimization ◽  
Reproduction Number ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Future Trend ◽  
Cumulative Number ◽  
Future Trends ◽  
Short Term ◽  
Swarm Optimization ◽  
Proposed Model

AbstractBackgroundThe first confirmed cases of COVID-19 in Iran were reported on February 19, 2020. The coronavirus expanded rapidly in all Iranian provinces and three waves of COVID-19 cases have been observed since the pandemic took effect and the fourth wave of Covid-19 cases will likely be observed soon. This study aimed to model the spread of COVID-19 in Iran and to estimate the epidemic parameters and to predict the short-term future trend of COVID-19 in Iran.MethodsWe proposed a modified SEIR epidemic spreading model and we used data from February 20, 2020, to April 9, 2021, on the number of cases reported by Iranian governments to fit the proposed model on the reported data. Particle Swarm Optimization (PSO) algorithm was employed to estimate the parameters of the proposed model and the numerical simulation results were obtained by Runge-Kutta method. The estimated parameters were employed to calculate the effective reproduction number and to predict the short-term future trends of COVID-19 cases.ResultsThe results indicated that the effective reproduction number has increased during Nowruz (Persian New Year) and it was estimated to be 1.28. Considering only two exposed cases as the initial cases in the model, the cumulative number of exposed cases was estimated to be 15,252,372 individuals since the beginning of the outbreak. The prediction of the short-term future trends of COVID-19 cases with different scenarios showed that another peak of the pandemic cases occurs in the next weeks. By immediate lockdown implementation the number of active infected cases was estimated to be 397,585.ConclusionDifferent scenarios of short-term prediction of the future trends of COVID-19 cases indicated that immediate strict social distancing policies need to be implemented to prevent a tremendous burden of the fourth major wave of COVID-19 infections on the health care system of Iran.

scholarly journals Virtual Angle Boundary-Aware Particle Swarm Optimization to Maximize the Coverage of Directional Sensor Networks

Sensors ◽  
2021 ◽  
Vol 21 (8) ◽  
pp. 2868
Author(s):  
Gong Cheng ◽  
Huangfu Wei
Keyword(s):  
Particle Swarm Optimization ◽  
Sensor Networks ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
The Internet ◽  
Directional Sensor Networks ◽  
Swarm Optimization ◽  
Coverage Optimization ◽  
Boundary Constraint ◽  
Directional Sensor

With the transition of the mobile communication networks, the network goal of the Internet of everything further promotes the development of the Internet of Things (IoT) and Wireless Sensor Networks (WSNs). Since the directional sensor has the performance advantage of long-term regional monitoring, how to realize coverage optimization of Directional Sensor Networks (DSNs) becomes more important. The coverage optimization of DSNs is usually solved for one of the variables such as sensor azimuth, sensing radius, and time schedule. To reduce the computational complexity, we propose an optimization coverage scheme with a boundary constraint of eliminating redundancy for DSNs. Combined with Particle Swarm Optimization (PSO) algorithm, a Virtual Angle Boundary-aware Particle Swarm Optimization (VAB-PSO) is designed to reduce the computational burden of optimization problems effectively. The VAB-PSO algorithm generates the boundary constraint position between the sensors according to the relationship among the angles of different sensors, thus obtaining the boundary of particle search and restricting the search space of the algorithm. Meanwhile, different particles search in complementary space to improve the overall efficiency. Experimental results show that the proposed algorithm with a boundary constraint can effectively improve the coverage and convergence speed of the algorithm.

scholarly journals Investigation on performance of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) in a combination of CFD modeling for prediction of fluid flow

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Meisam Babanezhad ◽  
Iman Behroyan ◽  
Ali Taghvaie Nakhjiri ◽  
Azam Marjani ◽  
Mashallah Rezakazemi ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Particle Swarm Optimization ◽  
Fuzzy Inference System ◽  
Fuzzy Inference ◽  
Bubble Column ◽  
Pso Algorithm ◽  
Cfd Modeling ◽  
Swarm Optimization ◽  
Inference System ◽  
Mesh Density

AbstractHerein, a reactor of bubble column type with non-equilibrium thermal condition between air and water is mechanistically modeled and simulated by the CFD technique. Moreover, the combination of the adaptive network (AN) trainer with the fuzzy inference system (FIS) as the artificial intelligence method calling ANFIS has already shown potential in the optimization of CFD approach. Although the artificial intelligence method of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) has a good background for optimizing the other fields of research, there are not any investigations on the cooperation of this method with the CFD. The PSOFIS can reduce all the difficulties and simplify the investigation by elimination of the additional CFD simulations. In fact, after achieving the best intelligence, all the predictions can be done by the PSOFIS instead of the massive computational efforts needed for CFD modeling. The first aim of this study is to develop the PSOFIS for use in the CFD approach application. The second one is to make a comparison between the PSOFIS and ANFIS for the accurate prediction of the CFD results. In the present study, the CFD data are learned by the PSOFIS for prediction of the water velocity inside the bubble column. The values of input numbers, swarm sizes, and inertia weights are investigated for the best intelligence. Once the best intelligence is achieved, there is no need to mesh refinement in the CFD domain. The mesh density can be increased, and the newer predictions can be done in an easier way by the PSOFIS with much less computational efforts. For a strong verification, the results of the PSOFIS in the prediction of the liquid velocity are compared with those of the ANFIS. It was shown that for the same fuzzy set parameters, the PSOFIS predictions are closer to the CFD in comparison with the ANFIS. The regression number (R) of the PSOFIS (0.98) was a little more than that of the ANFIS (0.97). The PSOFIS showed a powerful potential in mesh density increment from 9477 to 774,468 and accurate predictions for the new nodes independent of the CFD modeling.

scholarly journals A Time Series Sustainability Assessment of a Partial Energy Portfolio Transition

Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 141
Author(s):  
Jacob Hale ◽  
Suzanna Long
Keyword(s):  
Time Series ◽  
Electricity Generation ◽  
Sustainability Assessment ◽  
Moving Average ◽  
Time Series Prediction ◽  
State Level ◽  
Autoregressive Integrated Moving Average ◽  
Energy Transitions ◽  
Univariate Time Series ◽  
Partial Energy

Energy portfolios are overwhelmingly dependent on fossil fuel resources that perpetuate the consequences associated with climate change. Therefore, it is imperative to transition to more renewable alternatives to limit further harm to the environment. This study presents a univariate time series prediction model that evaluates sustainability outcomes of partial energy transitions. Future electricity generation at the state-level is predicted using exponential smoothing and autoregressive integrated moving average (ARIMA). The best prediction results are then used as an input for a sustainability assessment of a proposed transition by calculating carbon, water, land, and cost footprints. Missouri, USA was selected as a model testbed due to its dependence on coal. Of the time series methods, ARIMA exhibited the best performance and was used to predict annual electricity generation over a 10-year period. The proposed transition consisted of a one-percent annual decrease of coal’s portfolio share to be replaced with an equal share of solar and wind supply. The sustainability outcomes of the transition demonstrate decreases in carbon and water footprints but increases in land and cost footprints. Decision makers can use the results presented here to better inform strategic provisioning of critical resources in the context of proposed energy transitions.

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