scholarly journals Particle Swarm Optimization for Multiband Metamaterial Fractal Antenna

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Balamati Choudhury ◽  
Sangeetha Manickam ◽  
R. M. Jha
Keyword(s):  
Particle Swarm Optimization ◽  
Patch Antenna ◽  
Particle Swarm ◽  
Fractal Antenna ◽  
Performance Parameters ◽  
Self Similarity ◽  
Swarm Optimization ◽  
X Band ◽  
Different Types ◽  
Miniaturized Antenna

The property of self-similarity, recursive irregularity, and space filling capability of fractal antennas makes it useful for various applications in wireless communication, including multiband miniaturized antenna designs. In this paper, an effort has been made to use the metamaterial structures in conjunction with the fractal patch antenna, which resonates at six different frequencies covering both C and X band. Two different types of square SRR are loaded on the fractal antenna for this purpose. Particle swarm optimization (PSO) is used for optimization of these metamaterial structures. The optimized metamaterial structures, after loading upon, show significant increase in performance parameters such as bandwidth, gain, and directivity.

scholarly journals Optimal design of tricept parallel manipulator with particle swarm optimization using performance parameters

2021 ◽  
Vol 9 (2) ◽  
Author(s):  
Syed Saad Farooq ◽  
◽  
Aamer Ahmed Baqai ◽  
Muhammad Faizan Shah ◽  
◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Optimum Design ◽  
Particle Swarm ◽  
Parallel Manipulators ◽  
Optimized Design ◽  
Performance Parameters ◽  
Swarm Optimization ◽  
Design Variables ◽  
Design Values ◽  
Processing Effort

The parallel manipulators are skilled for their precision manufacturing but need optimized design to get maximum dexterity that will lead towards better industrial production rates. The 3-DOF tricept is chosen to utilize its maximum capabilities for its functionality. Three performance parameters conditioning index, workspace volume, and global conditioning index are used to obtain optimum design variables of tricept mechanism. With a view to compare them in terms of processing effort, particle swarm optimization (PSO) is applied here. Finally, multiobjective optimization with two strategies weighted and epsilon constraint is performed to control the different parameters simultaneously and also to give validation of previously obtained GA based optimum design values of tricept mechanism.

scholarly journals Enhance Clustering Algorithm Using Optimization

2020 ◽  
Vol 3 (9) ◽  
pp. 136-142
Author(s):  
Roshankumar Ramashish Maurya ◽  
Anand Khandare
Keyword(s):  
Particle Swarm Optimization ◽  
Unsupervised Learning ◽  
Clustering Algorithm ◽  
Particle Swarm ◽  
Unsupervised Clustering ◽  
Classification Problems ◽  
Swarm Optimization ◽  
Different Types

Unsupervised learning can reveal the structure of datasets without being concerned with any labels, K-means clustering is one such method. Traditionally the initial clusters have been selected randomly, with the idea that the algorithm will generate better clusters. However, studies have shown there are methods to improve this initial clustering as well as the K-means process. This paper examines these results on different types of datasets to study if these results hold for all types of data. Another method that is used for unsupervised clustering is the algorithm based on Particle Swarm Optimization. For the second part this paper studies the classic K-means based algorithm and a Hybrid K-means algorithm which uses PSO to improve the results from K-means. The hybrid K-means algorithms are compared to the standard K-means clustering on two benchmark classification problems. In this project we used Kaggle dataset to with different size (small, large and medium) for comparison PSO, k-means and k-means hybrid.

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