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PICA: Multi-Population Implementation of Parallel Imperialist Competitive Algorithms
KTH, School of Information and Communication Technology (ICT), Electronics and Embedded Systems.
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2016 (English)In: 2016 24TH EUROMICRO INTERNATIONAL CONFERENCE ON PARALLEL, DISTRIBUTED, AND NETWORK-BASED PROCESSING (PDP), Institute of Electrical and Electronics Engineers (IEEE), 2016, 248-255 p.Conference paper (Refereed)
Abstract [en]

The importance of optimization and NP problems solving cannot be over emphasized. The usefulness and popularity of evolutionary computing methods are also well established. There are various types of evolutionary methods that arc mostly sequential, and some others have parallel implementation. We propose a method to parallelize Imperialist Competitive Algorithm (Multi-Population). The algorithm has been implemented with MPI on two platforms and have tested our algorithms on a shared- memory and message passing architecture. An outstanding performance is obtained, which indicates that the method is efficient concern to speed and accuracy. In the second step, the proposed algorithm is compared with a set of existing well known parallel algorithms and is indicated that it obtains more accurate solutions in a lower time.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2016. 248-255 p.
, Euromicro Conference on Parallel Distributed and Network-Based Processing, ISSN 1066-6192
Keyword [en]
optimization, evolutionary computing, parallel approaches, ICA, parallel programing, multi-population, super linear performance
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
URN: urn:nbn:se:kth:diva-193254DOI: 10.1109/PDP.2016.93ISI: 000381810900031ScopusID: 2-s2.0-84968906499ISBN: 978-1-4673-8776-7OAI: diva2:1033906
24th Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), FEB 17-19, 2016, Heraklion, GREECE

QC 20161010

Available from: 2016-10-10 Created: 2016-09-30 Last updated: 2016-10-10Bibliographically approved

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Daneshtalab, Masoud
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