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The Distributed Stigmergic Algorithm for Multi-parameter Optimization

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Parallel Processing and Applied Mathematics (PPAM 2005)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 3911))

Abstract

The paper presents a new distributed Multilevel Ant Stigmergy Algorithm (MASA) for minimizing the power losses in an electric motor by optimizing the independent geometrical parameters of the rotor and the stator. The efficiency of the algorithm, in sequential form, to solve that particular optimization problem has already been shown in literature. However, even if this method offers good quality of solution, it still needs considerable computational time. With distributed implementation of the MASA the computation time is drastically decreased (from one day to few hours) without any noticeable loss in solution quality.

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Authors and Affiliations

  1. Computer Systems Department, Jožef Stefan Institute, Jamova 19, 1000, Ljubljana, Slovenia

    Jurij Šilc & Peter Korošec

Authors
  1. Jurij Šilc
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  2. Peter Korošec
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Editor information

Editors and Affiliations

  1. Institute of Computational and Information Sciences, Czestochowa University of Technology, Poland

    Roman Wyrzykowski

  2. Computer Science Department,, University of Tennessee, 37996-3450, Knoxville, TN, USA

    Jack Dongarra

  3. Poznan Supercomputing and Networking Center, Poland

    Norbert Meyer

  4. Informatics & Mathematical Modeling, Technical University of Denmark, 2800, Lyngby, DK, Denmark

    Jerzy Waśniewski

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© 2006 Springer-Verlag Berlin Heidelberg

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Šilc, J., Korošec, P. (2006). The Distributed Stigmergic Algorithm for Multi-parameter Optimization. In: Wyrzykowski, R., Dongarra, J., Meyer, N., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2005. Lecture Notes in Computer Science, vol 3911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752578_12

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  • DOI: https://doi.org/10.1007/11752578_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34141-3

  • Online ISBN: 978-3-540-34142-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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