scholarly journals CUDA-Based Particle Swarm Optimization in Reflectarray Antenna Synthesis

2020 ◽  
Vol 9 (2) ◽  
pp. 66-74
Author(s):  
A. Capozzoli ◽  
C. Curcio ◽  
A. Liseno
Keyword(s):  
Particle Swarm Optimization ◽  
Parallel Computing ◽  
Graphics Processing Units ◽  
Computing Time ◽  
Particle Swarm ◽  
Numerical Results ◽  
Antenna Synthesis ◽  
Swarm Optimization ◽  
Reflectarray Antenna ◽  
Graphics Processing

We show how reflectarray antenna synthesis can take profit from parallel computing on Graphics Processing Units (GPUs). The presented approach uses an implementation of Particle Swarm Optimization in CUDA language and accelerates the computation of the field radiated by the reflectarray using a GPU- mplemented Non-Uniform FFT routine. Numerical results show how computing time can be kept convenient for the application at hand.

scholarly journals Embedding parts in shape grammars using a parallel particle swarm optimization method on graphics processing units

2018 ◽  
Vol 32 (3) ◽  
pp. 256-268 ◽  
Author(s):  
Hacer Yalim Keles
Keyword(s):  
Particle Swarm Optimization ◽  
Graphics Processing Units ◽  
Particle Swarm ◽  
Search Space ◽  
Optimization Method ◽  
Processing Unit ◽  
Shape Grammars ◽  
Swarm Optimization ◽  
Parallel Particle Swarm Optimization ◽  
Graphics Processing

AbstractEmbedding emergent parts in shape grammars is computationally challenging. The first challenge is the representation of shapes, which needs to enable reinterpretation of parts regardless of the creation history of the shapes. The second challenge is the relevant part searching algorithm that provides an extensive exploration of the design space–time efficiently. In this work, we propose a novel method to solve both problems; we treat shapes as they are and use a parallel particle swarm optimization-based algorithm to compute emergent parts. The execution time of the proposed method is improved substantially by dividing the search space into small parts and carrying out searches in each part concurrently using a graphics processing unit. The experiments show that the proposed implementation detects emergent parts accurately and time efficiently.

The Optimal Design of Thermal Insulation on the Pipeline by Using Particle Swarm Optimization

2013 ◽  
Vol 834-836 ◽  
pp. 1397-1400
Author(s):  
Hong Yan Lv ◽  
Xiao Dong Si ◽  
Wen Zhen Wu
Keyword(s):  
Particle Swarm Optimization ◽  
Optimal Design ◽  
Thermal Insulation ◽  
Fuzzy Systems ◽  
Particle Swarm ◽  
Numerical Results ◽  
Function Optimization ◽  
Swarm Optimization ◽  
Nerve Network ◽  
Network Training

Particle swarm optimization is simple, easily achieved and do not need to adjust many parameters. And it doesn't need the gradient information. Now, this algorithm has been widely used in function optimization, nerve network training, fuzzy systems control and so on. In this paper, we use this algorithm to solve the optimal design of thermal insulation on the pipeline. Numerical results show that this algrithm can get more precise results than methods before used.

Global Path Planning for Mobile Robot Based on Improved Dijkstra Algorithm and Particle Swarm Optimization

2010 ◽  
Vol 26-28 ◽  
pp. 909-912 ◽  
Author(s):  
Nai Chao Chen ◽  
Ping He ◽  
Xian Ming Rui
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Mobile Robot ◽  
Computing Time ◽  
Particle Swarm ◽  
Dijkstra Algorithm ◽  
Swarm Optimization ◽  
Working Space ◽  
Global Path Planning ◽  
Limited Length

A novel method of improved Dijkstra algorithm and particle swarm optimization is proposed to evaluate global path planning for mobile robot. The first step is to make the MAKLINK graph which is used to describe the working space of mobile robot. The limited length value of free linkage line is conducted to substitute the constant weights in the adjacent matrix, which is well correlated with the fact that the number of paths is drastically less than that using the conventional Dijkstra method. Then the particle swarm optimization is adopted to investigate the global path from the several possible paths. Therefore, the proposed method facilitates reducing the computing time which enhances the efficiency of particle swarm optimization when performs the global path planning for mobile robot. Furthermore, simulation result is provided to verify the effectiveness and practicability.

scholarly journals Particle Swarm Optimization on Parallel Computers for Improving the Performance of a Gait Recognition System

2019 ◽  
Vol 9 (2) ◽  
pp. 193-201
Author(s):  
Shahla A. Abdulqader ◽  
Hasmek A. Krekorian
Keyword(s):  
Particle Swarm Optimization ◽  
Parallel Computing ◽  
Gait Recognition ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Recognition System ◽  
Parallel Computers ◽  
Recognition Time ◽  
Swarm Optimization ◽  
Swarm Size

In recent years, the gait recognition (GR) using particle swarm optimization (PSO) algorithm (OSO) has been execute very fast and accurate with single computer, but with the appearance of parallel computing (PC), it was necessary to use this technique to improve the results of GR. This study presents the use of parallel computing approaches (PCA) to implement PSO for a GR system (GRS) to decrease processing while maintaining reconstructed image quality. These approaches are: Codistributor and parallel cluster. Many experiments have been executed with recognition between the two approaches. The experimental results showed that increasing the PSO swarm size, decreasing number of iterations, and increasing number of workers used for the PCA can reduce recognition time and increase performance. Best results were obtained from implementing parallel computing with eight workers and 100 iterations. The execution time reached 4s and PSNR reached 44db. At the same time, the best results were obtained from PCL approach.

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