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  1. Swarm Intelligence
  2. Swarm Intelligence : Volume 6
  3. Swarm Intelligence : Volume 6, Issue 2, June 2012
  4. A speculative approach to parallelization in particle swarm optimization
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Swarm Intelligence : Volume 11
Swarm Intelligence : Volume 10
Swarm Intelligence : Volume 9
Swarm Intelligence : Volume 8
Swarm Intelligence : Volume 7
Swarm Intelligence : Volume 6
Swarm Intelligence : Volume 6, Issue 4, December 2012
Swarm Intelligence : Volume 6, Issue 3, September 2012
Swarm Intelligence : Volume 6, Issue 2, June 2012
A speculative approach to parallelization in particle swarm optimization
Distributed graph coloring: an approach based on the calling behavior of Japanese tree frogs
Brood comb construction by the stingless bees Tetragonula hockingsi and Tetragonula carbonaria
Swarm Intelligence : Volume 6, Issue 1, March 2012
Swarm Intelligence : Volume 5
Swarm Intelligence : Volume 4
Swarm Intelligence : Volume 3
Swarm Intelligence : Volume 2
Swarm Intelligence : Volume 1

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A speculative approach to parallelization in particle swarm optimization

Content Provider SpringerLink
Author Gardner, Matthew McNabb, Andrew Seppi, Kevin
Copyright Year 2011
Abstract Particle swarm optimization (PSO) has previously been parallelized primarily by distributing the computation corresponding to particles across multiple processors. In these approaches, the only benefit of additional processors is an increased swarm size. However, in many cases this is not efficient when scaled to very large swarm sizes (on very large clusters). Current methods cannot answer well the question: “How can 1000 processors be fully utilized when 50 or 100 particles is the most efficient swarm size?” In this paper we attempt to answer that question with a speculative approach to the parallelization of PSO that we refer to as SEPSO.In our approach, we refactor PSO such that the computation needed for iteration t+1 can be done concurrently with the computation needed for iteration t. Thus we can perform two iterations of PSO at once. Even with some amount of wasted computation, we show that this approach to parallelization in PSO often outperforms the standard parallelization of simply adding particles to the swarm. SEPSO produces results that are exactly equivalent to PSO; that is, SEPSO is a new method of parallelization and not a new PSO algorithm or variant.However, given this new parallelization model, we can relax the requirement of exactly reproducing PSO in an attempt to produce better results. We present several such relaxations, including keeping the best speculative position evaluated instead of the one corresponding to the standard behavior of PSO, and speculating several iterations ahead instead of just one. We show that these methods dramatically improve the performance of parallel PSO in many cases, giving speed ups of up to six compared to previous parallelization techniques.
Starting Page 77
Ending Page 116
Page Count 40
File Format PDF
ISSN 19353812
Journal Swarm Intelligence
Volume Number 6
Issue Number 2
e-ISSN 19353820
Language English
Publisher Springer US
Publisher Date 2011-12-21
Publisher Place Boston
Access Restriction One Nation One Subscription (ONOS)
Subject Keyword Parallel algorithms Optimization methods Particle swarm optimization Speculative decomposition Computer Systems Organization and Communication Networks ApplicationMathematics/Computational Methods of Engineering Computer Communication Networks Communications Engineering, Networks Artificial Intelligence (incl. Robotics)
Content Type Text
Resource Type Article
Subject Artificial Intelligence
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