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MRPSO: MapReduce particle swarm optimization

Published: 07 July 2007 Publication History

Abstract

In optimization problems involving large amounts of data, Particle Swarm Optimization (PSO) must be parallelized because individual function evaluations may take minutes or even hours. However, large-scale parallelization is difficult because programs must communicate efficiently, balance workloads and tolerate node failures. To address these issues, we present Map Reduce Particle Swarm Optimization(MRPSO), a PSO implementation based on Google's Map Reduce parallel programming model.

References

[1]
James Kennedy and Russell C. Eberhart. Particle swarm optimization. In International Conference on Neural Networks IV, pages 1942--1948, Piscataway, NJ, 1995. IEEE Service Center.
[2]
Jeffrey Dean and Sanjay Ghemawat. MapReduce: Simplified data processing on large clusters. Sixth Symposium on Operating System Design and Implementation, November 2004.

Cited By

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  • (2022)25 Years of Particle Swarm Optimization: Flourishing Voyage of Two DecadesArchives of Computational Methods in Engineering10.1007/s11831-022-09849-x30:3(1663-1725)Online publication date: 3-Dec-2022
  • (2017)Parallel Grouping Particle Swarm Optimization with Stream Processing Paradigm2017 IEEE 19th International Conference on High Performance Computing and Communications Workshops (HPCCWS)10.1109/HPCCWS.2017.00010(22-26)Online publication date: Dec-2017
  • (2017)Optimization of stream‐based live data migration strategy in the cloudConcurrency and Computation: Practice and Experience10.1002/cpe.429330:12Online publication date: 31-Aug-2017
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Published In

cover image ACM Conferences
GECCO '07: Proceedings of the 9th annual conference on Genetic and evolutionary computation
July 2007
2313 pages
ISBN:9781595936974
DOI:10.1145/1276958

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 July 2007

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Author Tags

  1. optimization
  2. parallelization
  3. swarm intelligence

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GECCO '07 Paper Acceptance Rate 266 of 577 submissions, 46%;
Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

View all
  • (2022)25 Years of Particle Swarm Optimization: Flourishing Voyage of Two DecadesArchives of Computational Methods in Engineering10.1007/s11831-022-09849-x30:3(1663-1725)Online publication date: 3-Dec-2022
  • (2017)Parallel Grouping Particle Swarm Optimization with Stream Processing Paradigm2017 IEEE 19th International Conference on High Performance Computing and Communications Workshops (HPCCWS)10.1109/HPCCWS.2017.00010(22-26)Online publication date: Dec-2017
  • (2017)Optimization of stream‐based live data migration strategy in the cloudConcurrency and Computation: Practice and Experience10.1002/cpe.429330:12Online publication date: 31-Aug-2017
  • (2016)MrsProceedings of the 6th Workshop on Python for High-Performance and Scientific Computing10.5555/3019083.3019093(76-85)Online publication date: 13-Nov-2016
  • (2016)Experimental Cloud Query Processing System for Social NetworksAnalyzing and Securing Social Networks10.1201/b19566-32(307-328)Online publication date: 31-Mar-2016
  • (2016)Mrs: High Performance MapReduce for Iterative and Asynchronous Algorithms in Python2016 6th Workshop on Python for High-Performance and Scientific Computing (PyHPC)10.1109/PyHPC.2016.014(76-85)Online publication date: Nov-2016
  • (2013)Web Portal for Matching Loan Requests and Investment Offers in Peer-To-Peer LendingInternational Journal of Web Portals10.4018/jwp.20130401025:2(17-31)Online publication date: 1-Apr-2013
  • (2013)Experimental Cloud Query Processing SystemDeveloping and Securing the Cloud10.1201/b15433-21(239-268)Online publication date: 10-Oct-2013
  • (2013)Scaling Genetic Programming for data classification using MapReduce methodology2013 World Congress on Nature and Biologically Inspired Computing10.1109/NaBIC.2013.6617851(132-139)Online publication date: Aug-2013
  • (2012)MrsProceedings of the 2012 SC Companion: High Performance Computing, Networking Storage and Analysis10.1109/SC.Companion.2012.84(600-608)Online publication date: 10-Nov-2012
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