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Parallel imperialist competitive algorithms
Abo Akad Univ, Finland..
Univ Turku, Finland..
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems.
Univ Turku, Finland..
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2018 (English)In: Concurrency and Computation, ISSN 1532-0626, E-ISSN 1532-0634, Vol. 30, no 7, article id e4393Article in journal (Refereed) Published
Abstract [en]

The importance of optimization and NP-problem solving cannot be overemphasized. The usefulness and popularity of evolutionary computing methods are also well established. There are various types of evolutionary methods; they are mostly sequential but some of them have parallel implementations as well. We propose a multi-population method to parallelize the Imperialist Competitive Algorithm. The algorithm has been implemented with the Message Passing Interface on 2 computer platforms, and we have tested our method based on shared memory and message passing architectural models. An outstanding performance is obtained, demonstrating that the proposed method is very efficient concerning both speed and accuracy. In addition, compared with a set of existing well-known parallel algorithms, our approach obtains more accurate results within a shorter time period.

Place, publisher, year, edition, pages
WILEY , 2018. Vol. 30, no 7, article id e4393
Keyword [en]
evolutionary computing, ICA, multi-population, parallel approaches, parallel programming, optimization, super-linear performance
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:mdh:diva-38895DOI: 10.1002/cpe.4393ISI: 000426769200006OAI: oai:DiVA.org:mdh-38895DiVA, id: diva2:1194220
Available from: 2018-03-29 Created: 2018-03-29 Last updated: 2018-03-29Bibliographically approved

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Daneshtalab, Masoud

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