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.

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.

WNN Model Based on Particle Swarm Optimization for Fault Diagnosis in Analog Circuit

2013 ◽  
Vol 427-429 ◽  
pp. 1048-1051
Author(s):  
Xu Sheng Gan ◽  
Hao Lin Cui ◽  
Ya Rong Wu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Convergence Rate ◽  
Analog Circuit ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Wavelet Neural Network ◽  
Swarm Optimization ◽  
Fast Convergence Rate

In order to diagnose the fault in analog circuit correctly, a Wavelet Neural Network (WNN) method is proposed that uses the Particle Swarm Optimization (PSO) algorithm to optimize the network parameters. For the improvement of convergence rate in WNN based on PSO algorithm, a compressing method in research space is introduced into the traditional PSO algorithm to improve the convergence in WNN training. The simulation shows that the proposed method has a good diagnosis with fast convergence rate for the fault in analog circuit.

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.

Enhancing lifetime of wireless multimedia sensor networks using modified lion algorithm–based image transmission model

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mahesh P. Wankhade ◽  
KC Jondhale
Keyword(s):  
Energy Consumption ◽  
Convergence Rate ◽  
Cost Function ◽  
Optimal Selection ◽  
Wireless Multimedia ◽  
Swarm Optimization ◽  
Content Type ◽  
Multimedia Sensor Networks ◽  
Lion Algorithm ◽  
Proposed Model

PurposeIn the past few decades, the wireless sensor network (WSN) has become the more vital one with the involvement of the conventional WSNs and wireless multimedia sensor networks (WMSNs). The network that is composed of low-power, small-size, low-cost sensors is said to be WSN. Here, the communication information is handled using the multiple hop and offers only a simple sensing data, such as humidity, temperature and so on, whereas WMSNs are referred as the distributed sensing networks that are composed of video cameras, which contain the sector sense area. These WMSNs can send, receive and process the video information data, which is more intensive and complicated by wrapping with wireless transceiver. The WSNs and the WMSNs are varied in terms of their characteristic of turnablity and directivity.Design/methodology/approachThe main intention of this paper is to maximize the lifetime of network with reduced energy consumption by using an advanced optimization algorithm. The optimal transmission radius is achieved by optimizing the system parameter to transmit the sensor information to the consequent sensor nodes, which are contained within the range. For this optimal selection, this paper proposes a new modified lion algorithm (LA), the so-called cub pool-linked lion algorithm (CLA). The next contribution is on the optimal selection of cluster head (CH) by the proposed algorithm. Finally, the performance of proposed model is validated and compared over the other traditional methods in terms of network energy, convergence rate and alive nodes.FindingsThe proposed model's cost function relies in the range of 74–78. From the result, it is clear that at sixth iteration, the proposed model’s performance attains less cost function, that is, 11.14, 9.78, 7.26, 4.49 and 4.13% better than Genetic Algorithm (GA), Dragonfly Algorithm (DA), Particle Swarm Optimization (PSO), Glowworm Swarm Optimization (GSO) and Firefly (FF), correspondingly. The performance of the proposed model at eighth iteration is 14.15, 7.96, 4.36, 7.73, 7.38 and 3.39% superior to GA, DA, PSO, GSO, FF and LA, correspondingly with less convergence rate.Originality/valueThis paper presents a new optimization technique for increasing the network lifetime with reduced energy consumption. This is the first work that utilizes CLA for optimization problems.

An improved artificial fish swarm algorithm optimized by particle swarm optimization algorithm with extended memory

Kybernetes ◽  
2016 ◽  
Vol 45 (2) ◽  
pp. 210-222 ◽  
Author(s):  
Qichang Duan ◽  
Mingxuan Mao ◽  
Pan Duan ◽  
Bei Hu
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Search Process ◽  
Swarm Optimization ◽  
Content Type ◽  
Memory Factor ◽  
Artificial Fish Swarm Algorithm ◽  
Artificial Fish Swarm

Purpose – The purpose of this paper is to solve the problem that the standard particle swarm optimization (PSO) algorithm has a low success rate when applied to the optimization of multi-dimensional and multi-extreme value functions, the authors would introduce the extended memory factor to the PSO algorithm. Furthermore, the paper aims to improve the convergence rate and precision of basic artificial fish swarm algorithm (FSA), a novel FSA optimized by PSO algorithm with extended memory (PSOEM-FSA) is proposed. Design/methodology/approach – In PSOEM-FSA, the extended memory for PSO is introduced to store each particle’ historical information comprising of recent places, personal best positions and global best positions, and a parameter called extended memory effective factor is employed to describe the importance of extended memory. Then, stability region of its deterministic version in a dynamic environment is analyzed by means of the classic discrete control theory. Furthermore, the extended memory factor is applied to five kinds of behavior pattern for FSA, including swarming, following, remembering, communicating and searching. Findings – The paper proposes a new intelligent algorithm. On the one hand, this algorithm makes the fish swimming have the characteristics of the speed of inertia; on the other hand, it expands behavior patterns for the fish to choose in the search process and achieves higher accuracy and convergence rate than PSO-FSA, owning to extended memory beneficial to direction and purpose during search. Simulation results verify that these improvements can reduce the blindness of fish search process, improve optimization performance of the algorithm. Research limitations/implications – Because of the chosen research approach, the research results may lack persuasion. In the future study, the authors will conduct more experiments to understand the behavior of PSOEM-FSA. In addition, there are mainly two aspects that the performance of this algorithm could be further improved. Practical implications – The proposed algorithm can be used to many practical engineering problems such as tracking problems. Social implications – The authors hope that the PSOEM-FSA can increase a branch of FSA algorithm, and enrich the content of the intelligent algorithms to some extent. Originality/value – The novel optimized FSA algorithm proposed in this paper improves the convergence speed and searching precision of the ordinary FSA to some degree.

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.

scholarly journals Estimating one-diode-PV model using autonomous groups particle swarm optimization

2021 ◽  
Vol 10 (1) ◽  
pp. 166
Author(s):  
Mohammad AlShabi ◽  
Chaouki Ghenai ◽  
Maamar Bettayeb ◽  
Fahad Faraz Ahmad
Keyword(s):  
Particle Swarm Optimization ◽  
Convergence Rate ◽  
Mean Squared Error ◽  
Particle Swarm ◽  
Absolute Error ◽  
Pso Algorithm ◽  
Maximum Absolute Error ◽  
Swarm Optimization ◽  
The Social ◽  
The One

In this paper, the one-diode model of a photovoltaic PV solar cell (PVSC) is estimated for an experimental characteristic curves data by using a recently proposed version of the Particle Swarm Optimization (PSO) algorithm, which is known as the Autonomous Groups Particles Swarm Optimization (PSOAG). This meta-heuristic algorithm is used to identify the model of the PVSC. The PSOAG divides the particles into groups and then, uses different functions to tune the social and cognitive parameters of these groups. This is done to show the individuals’ diversity inside the swarm. Although, these individuals do their duties as part of the society, they are not similar in terms of intelligence and ability. By using these groups, the performance of the PSO is improved in terms of convergence rate and escaping the local minima/maxima. Six versions of PSOAG algorithms were developed in this work. Therefore, nine versions of PSOAG, including these six algorithms and three newly developed PSOAG reported previously, will be used in this research to cover more social’s behaviors. The results are compared to the original PSO and other versions of PSO like conventional and Asymmetric Time-varying Accelerated Coefficient PSOs, and the improved PSO. The result shows that the proposed methods improve the performance by up to 14% in terms of root mean squared error and maximum absolute error, and by up to 20% in term of convergence rate, when these were compared to the best results obtained from the other algorithms.

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.

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