scholarly journals A Novel Model on Curve Fitting and Particle Swarm Optimization for Vertical Handover in Heterogeneous Wireless Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-16 ◽  
Author(s):  
Shidrokh Goudarzi ◽  
Wan Haslina Hassan ◽  
Mohammad Hossein Anisi ◽  
Seyed Ahmad Soleymani ◽  
Parvaneh Shabanzadeh
Keyword(s):  
Particle Swarm Optimization ◽  
Curve Fitting ◽  
Particle Swarm ◽  
Signal Strength ◽  
Pso Algorithm ◽  
Vertical Handover ◽  
Received Signal Strength ◽  
Network Selection ◽  
Received Signal Strength Indicator ◽  
Swarm Optimization

The vertical handover mechanism is an essential issue in the heterogeneous wireless environments where selection of an efficient network that provides seamless connectivity involves complex scenarios. This study uses two modules that utilize the particle swarm optimization (PSO) algorithm to predict and make an intelligent vertical handover decision. In this paper, we predict the received signal strength indicator parameter using the curve fitting based particle swarm optimization (CF-PSO) and the RBF neural networks. The results of the proposed methodology compare the predictive capabilities in terms of coefficient determination (R2) and mean square error (MSE) based on the validation dataset. The results show that the effect of the model based on the CF-PSO is better than that of the model based on the RBF neural network in predicting the received signal strength indicator situation. In addition, we present a novel network selection algorithm to select the best candidate access point among the various access technologies based on the PSO. Simulation results indicate that using CF-PSO algorithm can decrease the number of unnecessary handovers and prevent the “Ping-Pong” effect. Moreover, it is demonstrated that the multiobjective particle swarm optimization based method finds an optimal network selection in a heterogeneous wireless environment.

scholarly journals A precise ultra high frequency partial discharge location method for switchgear based on received signal strength ranging

2020 ◽  
Vol 16 (5) ◽  
pp. 155014772090363
Author(s):  
Jiajia Song ◽  
Jinbo Zhang ◽  
Xinnan Fan
Keyword(s):  
Particle Swarm Optimization ◽  
High Frequency ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Received Signal Strength Indicator ◽  
Swarm Optimization ◽  
Ultra High Frequency ◽  
Discharge Location

Partial discharges are the main insulation defects encountered in gas-insulated switchgears. When it occurs inside the gas-insulated switchgear cavity, it degrades insulation, and, sooner or later, causes a breakdown. Therefore, it is important to discover insulation defects as early as possible, locate the discharge, and perform both defect identification and maintenance. Current ultra high frequency-based partial discharge location methods mainly use time delay. To obtain accurate delay times, however, a very high sampling rate is needed, which requires expensive hardware and greatly limits its application. Therefore, in this article, a localization method based on received signal strength indicator ranging is proposed, and location estimation is carried out. An easily implementable particle swarm optimization algorithm with high positioning accuracy is selected to compensate for the low positioning accuracy of current received signal strength indicator ranging methods. To further improve positioning accuracy, the convergence conditions of the particle swarm optimization are investigated, and, considering their constraints, an improved particle swarm optimization algorithm is proposed. By combining the characteristics of ultra high frequency wireless sensor array positioning, the particle size is optimized. The simulation results show that the location accuracy using the ultra high frequency switchgear partial discharge location method based on received signal strength indicator ranging with the improved particle swarm optimization algorithm performs significantly better.

scholarly journals Particle swarm optimization–based minimum residual algorithm for mobile robot localization in indoor environment

2017 ◽  
Vol 14 (5) ◽  
pp. 172988141772927 ◽  
Author(s):  
Yunzhou Zhang ◽  
Hang Hu ◽  
Wenyan Fu ◽  
Hao Jiang
Keyword(s):  
Particle Swarm Optimization ◽  
Mobile Robot ◽  
Indoor Environment ◽  
Particle Swarm ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Robot Localization ◽  
Received Signal Strength Indicator ◽  
Swarm Optimization ◽  
Minimum Residual

For indoor mobile robots, many localization systems based on wireless sensor network have been reported. Received signal strength indicator is often used for distance measurement. However, the value of received signal strength indicator always has large fluctuation because radio signal is easily influenced by environmental factors. This will bring adverse effect on the distance measurement and deteriorate the performance of robot localization. In this article, the measured data are dealt with weighted recursive filter, which can depress the measurement noise effectively. In the linearization procedure, the least square method often causes additional error because it seriously relies on anchor nodes. Therefore, a minimum residual localization algorithm based on particle swarm optimization is proposed for a mobile robot running in indoor environment. With continuous optimization and update of particle swarm, the position that gets the best solution of objective function can be adopted as the final estimated position. Experiment results show that the proposed algorithm, compared with traditional algorithms, can attain better localization accuracy and is closer to Cramer–Rao lower bound.

scholarly journals Particle swarm optimization application for multiple attribute decision making in vertical handover in heterogenous wireless networks

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Mishal Al-Gharabally ◽  
◽  
Ali F. Almutairi ◽  
Ayed A. Salman ◽  
◽  
...  
Keyword(s):  
Decision Making ◽  
Wireless Networks ◽  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Vertical Handover ◽  
Multiple Attribute Decision Making ◽  
Network Selection ◽  
Swarm Optimization ◽  
Multiple Attribute ◽  
Handover Process

In wireless heterogenous networks, mobile terminals are covered by different wireless networks with varying quality of services to ensure the delivery of different classes of services. In this paper, Particle swarm optimization (PSO) was applied to the distance to ideal alternative (DIA) technique in the framework of network selection in a heterogenous wireless network. The PSO was applied to overcome the subjectivity and bias in the weights’ assignment process used in multiple attribute decision making (MADM). The PSO was utilized to optimize the weights of the DIA method through the maximization of the absolute value of the summation of the ranking differences among candidate networks. In this regard, two different optimization functions were introduced and used to generate the optimum weights. The performance of the PSO-based handover for the DIA method was investigated in terms of ranking difference, ranking abnormalities, and network selection. The results show that the proposed PSO-based weights’ assignment technique increased the ranking difference and reduced the ranking abnormalities without degrading the network selection when compared to the conventional DIA technique. The results of this paper are expected to widen the application of the DIA method and other MADM techniques to the handover process in wireless networks and other decision-based challenges in other fields.

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.

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 Particle swarm optimization algorithm for the optimization of rescue task allocation with uncertain time constraints

2021 ◽  
Author(s):  
Na Geng ◽  
Zhiting Chen ◽  
Quang A. Nguyen ◽  
Dunwei Gong
Keyword(s):  
Particle Swarm Optimization ◽  
Task Allocation ◽  
Optimal Algorithm ◽  
Particle Swarm ◽  
Evolutionary Process ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Fitness Evaluation ◽  
Modified Particle Swarm Optimization ◽  
Uncertain Time

AbstractThis paper focuses on the problem of robot rescue task allocation, in which multiple robots and a global optimal algorithm are employed to plan the rescue task allocation. Accordingly, a modified particle swarm optimization (PSO) algorithm, referred to as task allocation PSO (TAPSO), is proposed. Candidate assignment solutions are represented as particles and evolved using an evolutionary process. The proposed TAPSO method is characterized by a flexible assignment decoding scheme to avoid the generation of unfeasible assignments. The maximum number of successful tasks (survivors) is considered as the fitness evaluation criterion under a scenario where the survivors’ survival time is uncertain. To improve the solution, a global best solution update strategy, which updates the global best solution depends on different phases so as to balance the exploration and exploitation, is proposed. TAPSO is tested on different scenarios and compared with other counterpart algorithms to verify its efficiency.

scholarly journals Multiple RFI Sources Location Method Combining Two-Dimensional ESPRIT DOA Estimation and Particle Swarm Optimization for Spaceborne SAR

2021 ◽  
Vol 13 (6) ◽  
pp. 1207
Author(s):  
Junfei Yu ◽  
Jingwen Li ◽  
Bing Sun ◽  
Yuming Jiang ◽  
Liying Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Geographic Location ◽  
Doa Estimation ◽  
Pso Algorithm ◽  
Two Dimensional ◽  
Swarm Optimization ◽  
Location Method ◽  
Esprit Algorithm ◽  
To Receive

Synthetic aperture radar (SAR) systems are susceptible to radio frequency interference (RFI). The existence of RFI will cause serious degradation of SAR image quality and a huge risk of target misjudgment, which makes the research on RFI suppression methods receive widespread attention. Since the location of the RFI source is one of the most vital information for achieving RFI spatial filtering, this paper presents a novel location method of multiple independent RFI sources based on direction-of-arrival (DOA) estimation and the non-convex optimization algorithm. It deploys an L-shaped multi-channel array on the SAR system to receive echo signals, and utilizes the two-dimensional estimating signal parameter via rotational invariance techniques (2D-ESPRIT) algorithm to estimate the positional relationship between the RFI source and the SAR system, ultimately combines the DOA estimation results of multiple azimuth time to calculate the geographic location of RFI sources through the particle swarm optimization (PSO) algorithm. Results on simulation experiments prove the effectiveness of the proposed method.

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