Parallel Implementation of Particle Swarm Optimization Variants Using Graphics Processing Unit Platform
Publish Year: 1395
نوع سند: مقاله ژورنالی
زبان: English
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شناسه ملی سند علمی:
JR_IJE-30-1_003
تاریخ نمایه سازی: 9 خرداد 1396
Abstract:
There are different variants of Particle Swarm Optimization (PSO) algorithm such as Adaptive Particle Swarm Optimization (APSO) and Particle Swarm Optimization with an Aging Leader and Challengers (ALC-PSO). These algorithms improve the performance of PSO in terms of finding the best solution and accelerating the convergence speed. However, these algorithms are computationally intensive. The goal of this paper is high performance implementations of Traditional PSO (TPSO), APSO and ALCPSOusing CUDA technology. We have implemented these three algorithms on both central processing unit (CPU) and graphics processing unit (GPU) in order to analyze and improve their performance and reduce their computational times. We have achieved speedups up to 14.5x, 31x, and 152x, for GPUTPSO, GPU-ALCPSO , and GPU-APSO, respectively. In addition, different number of threads has been chosen in order to find an appropriate number of threads per block for both APSO and ALC-PSOalgorithms. Our experimental results show that the best choice for number of threads per block depends on number of existing variables and constants in each algorithm and number of registers per multiprocessor
Keywords:
Particle Swarm Optimization , Adaptive Particle Swarm Optimization , Particle Swarm Optimization with an Aging , Leader and Challengers , Graphics Processing Unit
Authors
S Jam
Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran
A Shahbahrami
Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran
S.H.S Ziyabari
Department of Computer Engineering, Faculty of Engineering, University of Guilan, Rasht, Iran