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Random Key Self-Organizing Migrating Algorithm for Permutation Problems

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F19%3A10244290" target="_blank" >RIV/61989100:27240/19:10244290 - isvavai.cz</a>

  • Result on the web

    <a href="https://ieeexplore.ieee.org/document/8790322" target="_blank" >https://ieeexplore.ieee.org/document/8790322</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1109/CEC.2019.8790322" target="_blank" >10.1109/CEC.2019.8790322</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Random Key Self-Organizing Migrating Algorithm for Permutation Problems

  • Original language description

    Self-organizing migrating algorithm (SOMA) is a modern stochastic optimization algorithm. It is built upon the principles of evolutionary and swarm computation and has been successfully applied to a variety of theoretical and practical optimization problems. The candidate solutions in SOMA are real-valued and the use of the algorithm for continuous optimization is straightforward. Its application to combinatorial optimization, on the other hand, requires a translation of candidate solutions from continuous search space to discrete problem solution space. In this work, a version of SOMA suitable for permutation problems is proposed and evaluated on two well-known hard permutation problems.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10200 - Computer and information sciences

Result continuities

  • Project

    Result was created during the realization of more than one project. More information in the Projects tab.

  • Continuities

    P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2019

  • Confidentiality

    S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Data specific for result type

  • Article name in the collection

    2019 IEEE Congress on Evolutionary Computation, CEC 2019 - Proceedings

  • ISBN

    978-1-72812-153-6

  • ISSN

  • e-ISSN

  • Number of pages

    8

  • Pages from-to

    2878-2885

  • Publisher name

    IEEE

  • Place of publication

    Piscataway

  • Event location

    Wellington

  • Event date

    Jun 10, 2019

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article

    000502087102115