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Self-organizing migrating algorithm with clustering-aided migration

The result's identifiers

  • Result code in IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F20%3A63526752" target="_blank" >RIV/70883521:28140/20:63526752 - isvavai.cz</a>

  • Result on the web

    <a href="https://dl.acm.org/doi/10.1145/3377929.3398129" target="_blank" >https://dl.acm.org/doi/10.1145/3377929.3398129</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1145/3377929.3398129" target="_blank" >10.1145/3377929.3398129</a>

Alternative languages

  • Result language

    angličtina

  • Original language name

    Self-organizing migrating algorithm with clustering-aided migration

  • Original language description

    This paper proposes a novel migration strategy for Self-organizing Migrating Algorithm (SOMA), which combines advantages of the explorative All-To-Random migration with new exploitation focused All-To-Cluster-Leaders strategy. The main goal of this novel innovation to SOMA is to deliver competitive results, not only on the latest CEC 2020 benchmark set on a single objective bound-constrained numerical optimization. The proposed algorithm variant was titled SOMA-CL, and it has manifested notable potential in such demanding challenges. The results of the proposed algorithm were compared and tested for statistical significance against two other SOMA variants. © 2020 ACM.

  • Czech name

  • Czech description

Classification

  • Type

    D - Article in proceedings

  • CEP classification

  • OECD FORD branch

    10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Result continuities

  • Project

  • Continuities

    S - Specificky vyzkum na vysokych skolach

Others

  • Publication year

    2020

  • Confidentiality

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

Data specific for result type

  • Article name in the collection

    GECCO 2020 Companion - Proceedings of the 2020 Genetic and Evolutionary Computation Conference Companion

  • ISBN

    978-145037127-8

  • ISSN

  • e-ISSN

  • Number of pages

    7

  • Pages from-to

    1441-1447

  • Publisher name

    Association for Computing Machinery

  • Place of publication

    New York

  • Event location

    Cancun

  • Event date

    Jul 8, 2020

  • Type of event by nationality

    WRD - Celosvětová akce

  • UT code for WoS article