Optimization and parametric analysis of slotted microstrip antenna using particle swarm optimization and curve fitting

2021 ◽  
Author(s):  
Ramesh Kumar Verma ◽  
Dinesh Kumar Srivastava
Keyword(s):  
Particle Swarm Optimization ◽  
Curve Fitting ◽  
Microstrip Antenna ◽  
Parametric Analysis ◽  
Particle Swarm ◽  
Swarm Optimization

Design of a dual-band microstrip antenna using particle swarm optimization with curve fitting

2014 ◽  
Vol 69 (11-12) ◽  
pp. 633-640 ◽  
Author(s):  
Jun-Jiat Tiang ◽  
Mohammad Tariqul Islam ◽  
Norbahiah Misran ◽  
Mandeep Jit Singh
Keyword(s):  
Particle Swarm Optimization ◽  
Curve Fitting ◽  
Microstrip Antenna ◽  
Particle Swarm ◽  
Dual Band ◽  
Swarm Optimization

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.

B-Spline Curve Fitting Based on Adaptive Particle Swarm Optimization Algorithm

2010 ◽  
Vol 20-23 ◽  
pp. 1299-1304 ◽  
Author(s):  
Yue Hong Sun ◽  
Zhao Ling Tao ◽  
Jian Xiang Wei ◽  
De Shen Xia
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Curve Fitting ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Control Points ◽  
Swarm Optimization ◽  
B Spline ◽  
Spline Curve

For fitting of ordered plane data by B-spline curve with the least squares, the genetic algorithm is generally used, accompanying the optimization on both the data parameter values and the knots to result in good robust, but easy to fall into local optimum, and without improved fitting precision by increasing the control points of the curve. So what we have done are: combining the particle swarm optimization algorithm into the B-spline curve fitting, taking full advantage of the distribution characteristic for the data, associating the data parameters with the knots, coding simultaneously the ordered data parameter and the number of the control points of the B-spline curve, proposing a new fitness function, dynamically adjusting the number of the control points for the B-spline curve. Experiments show the proposed particle swarm optimization method is able to adaptively reach the optimum curve much faster with much better accuracy accompanied less control points and less evolution times than the genetic algorithm.

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