Scheduled System Maintenance:
On Monday, April 27th, IEEE Xplore will undergo scheduled maintenance from 1:00 PM - 3:00 PM ET (17:00 - 19:00 UTC). No interruption in service is anticipated.
By Topic

Parallel PSO using MapReduce

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)

In optimization problems involving large amounts of data, such as web content, commercial transaction information, or bioinformatics data, individual function evaluations may take minutes or even hours. particle swarm optimization (PSO) must be parallelized for such functions. However, large-scale parallel programs must communicate efficiently, balance work across all processors, and address problems such as failed nodes. We present mapreduce particle swarm optimization (MRPSO), a PSO implementation based on the mapreduce parallel programming model. We describe MapReduce and show how PSO can be naturally expressed in this model, without explicitly addressing any of the details of parallelization. We present a benchmark function for evaluating MRPSO and note that MRPSO is not appropriate for optimizing easily evaluated functions. We demonstrate that MRPSO scales to 256 processors on moderately difficult problems and tolerates node failures.

Published in:

Evolutionary Computation, 2007. CEC 2007. IEEE Congress on

Date of Conference:

25-28 Sept. 2007