Open Access
Particle Swarm Optimization: A Hardware Implementation.
Parviz Palangpour,Ganesh K. Venayagamoorthy,Scott C. Smith +2 more
- 01 Jan 2009
pp 134-139
TL;DR: A pipelined architecture for hardware PSO implementation is presented and an execution speedup of several orders of magnitude is observed.
read more
Abstract: Particle Swarm Optimization (PSO) is a popular population-based optimization algorithm. While PSO has been shown to perform well in a large variety of problems, PSO is typically implemented in software. Population-based optimization algorithms such as PSO are well suited for execution in parallel stages. This allows PSO to be implemented directly in hardware and achieve much faster execution times than possible in software. In this paper, a pipelined architecture for hardware PSO implementation is presented. Benchmark functions solved by software and hardware PSO implementations are compared. The hardware PSO design is implemented on a Xilinx Virtex-II Pro Development Kit for evaluation. By implementing PSO directly on hardware an execution speedup of several orders of magnitude is observed.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
Particle Swarm Optimization on FPGA
Monia Ettouil,Habib Smei,Abderrazak Jemai +2 more
- 01 Dec 2018
TL;DR: This paper presents a comparative study of these various solutions of particle swarm optimization using a codesign methodology which adjusts performance parameters at design time and focuses on the FPGA ones.
8
Applying Swarm Optimization Techniques to Calculate Execution Time for Software Modules
TL;DR: The results show that PPSO algorithm is more efficient in speed and time compared to MCWA and PSO algorithm for calculating the execution time.
FPGA implementation of PSO algorithm and neural networks
Parviz Palangpour
- 01 Jan 2010
TL;DR: In this article, a pipelined architecture for hardware PSO implementation is presented, and benchmark functions are compared between software and FPGA hardware implementations of PSO and NN.
Bio-inspired self-aware fault-tolerant routing protocol for network-on-chip architectures using Particle Swarm Optimization
TL;DR: This study demonstrates that the proposed BISFTRP routing protocol can converge to a global optimum, minimal routing path in real time, in the presence of network congestion and faulty routers and links.
4
Using IoT in breakdown tolerance: PSO solving FJSP
Maroua Nouiri,Abderrazak Jemai,Ahmed Chiheb Ammari,Abdelghani Bekrar,Damien Trentesaux,Smail Niar +5 more
TL;DR: An improved multi agent particle swarm optimization is presented and implemented on physically distributed system composed of embedded systems to solve the FJSP under machine breakdowns and addresses also IoT challenges related to minimizing communication overheads.
3
References
A new optimizer using particle swarm theory
Russell C. Eberhart,James Kennedy +1 more
- 04 Oct 1995
TL;DR: The optimization of nonlinear functions using particle swarm methodology is described and implementations of two paradigms are discussed and compared, including a recently developed locally oriented paradigm.
16.4K
A discrete binary version of the particle swarm algorithm
James Kennedy,Russell C. Eberhart +1 more
- 12 Oct 1997
TL;DR: The paper reports a reworking of the particle swarm algorithm to operate on discrete binary variables, where trajectories are changes in the probability that a coordinate will take on a zero or one value.
5K
A novel binary particle swarm optimization
Mojtaba Ahmadieh Khanesar,Mohammad Teshnehlab,Mahdi Aliyari Shoorehdeli +2 more
- 27 Jun 2007
TL;DR: This algorithm is shown to be a better interpretation of continuous PSO into discrete PSO than the older versions and a number of benchmark optimization problems are solved using this concept and quite satisfactory results are obtained.
431
FPGA implementation of neighborhood-of-four cellular automata random number generators
Barry Shackleford,Motoo Tanaka,Richard J. Carter,Greg Snider +3 more
- 24 Feb 2002
TL;DR: Random number generators (RNGs) based upon neighborhood-of-four cellular automata (CA) with asymmetrical, non-local connections are explored and a number of RNGs that pass Marsaglia's rigorous Diehard suite of random number tests have been discovered.
73
Applying Particle Swarm Optimization to Prioritizing Test Cases for Embedded Real Time Software Retesting
Khin Haymar Saw Hla,Young-Sik Choi,Jong Sou Park +2 more
- 08 Jul 2008
TL;DR: The empirical results show that by using the PSO algorithm, the test cases can be prioritized in the test suites with their new best positions effectively and efficiently.
58