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Self-organizing migrating algorithm using covariance matrix adaptation evolution strategy for dynamic constrained optimization

Identifikátory výsledku

  • Kód výsledku v IS VaVaI

    <a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F21%3A10247739" target="_blank" >RIV/61989100:27240/21:10247739 - isvavai.cz</a>

  • Nalezeny alternativní kódy

    RIV/61989100:27740/21:10247739

  • Výsledek na webu

    <a href="https://www.sciencedirect.com/science/article/pii/S2210650221000973" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2210650221000973</a>

  • DOI - Digital Object Identifier

    <a href="http://dx.doi.org/10.1016/j.swevo.2021.100936" target="_blank" >10.1016/j.swevo.2021.100936</a>

Alternativní jazyky

  • Jazyk výsledku

    angličtina

  • Název v původním jazyce

    Self-organizing migrating algorithm using covariance matrix adaptation evolution strategy for dynamic constrained optimization

  • Popis výsledku v původním jazyce

    The dynamic constrained optimization problems can be a challenge for the optimization algorithms. They must tackle global optimum detection, as well as the change of the environment. Recently, a novel test suite for dynamic constrained optimization was introduced. Furthermore, three well-performed evolutionary algorithms were compared based on it. The experimental results show that each algorithm performed best for a different type of optimization problem. The objective of our work was to develop an algorithm reflecting requirements arising from the novel test suite and regarding the results provided by the tested algorithms. In this work, we present a novel evolutionary algorithm for dynamic constrained optimization. The algorithm hybridizes the self-organizing migrating algorithm and the covariance matrix adaptation evolution strategy with constraints handling approach. To avoid premature convergence, the best solutions representing feasible regions do not affect the rest of the population. Two clustering methods, exclusion radius, and quantum particles are used to preserve population diversity. The performance is evaluated on the recently published test suite and compared to the three state-of-the-art algorithms. The presented algorithm outperformed these algorithms in most test cases, which indicates the efficiency of the utilized mechanisms. (C) 2021 The Author(s)

  • Název v anglickém jazyce

    Self-organizing migrating algorithm using covariance matrix adaptation evolution strategy for dynamic constrained optimization

  • Popis výsledku anglicky

    The dynamic constrained optimization problems can be a challenge for the optimization algorithms. They must tackle global optimum detection, as well as the change of the environment. Recently, a novel test suite for dynamic constrained optimization was introduced. Furthermore, three well-performed evolutionary algorithms were compared based on it. The experimental results show that each algorithm performed best for a different type of optimization problem. The objective of our work was to develop an algorithm reflecting requirements arising from the novel test suite and regarding the results provided by the tested algorithms. In this work, we present a novel evolutionary algorithm for dynamic constrained optimization. The algorithm hybridizes the self-organizing migrating algorithm and the covariance matrix adaptation evolution strategy with constraints handling approach. To avoid premature convergence, the best solutions representing feasible regions do not affect the rest of the population. Two clustering methods, exclusion radius, and quantum particles are used to preserve population diversity. The performance is evaluated on the recently published test suite and compared to the three state-of-the-art algorithms. The presented algorithm outperformed these algorithms in most test cases, which indicates the efficiency of the utilized mechanisms. (C) 2021 The Author(s)

Klasifikace

  • Druh

    J<sub>imp</sub> - Článek v periodiku v databázi Web of Science

  • CEP obor

  • OECD FORD obor

    10200 - Computer and information sciences

Návaznosti výsledku

  • Projekt

  • Návaznosti

    S - Specificky vyzkum na vysokych skolach

Ostatní

  • Rok uplatnění

    2021

  • Kód důvěrnosti údajů

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

Údaje specifické pro druh výsledku

  • Název periodika

    Swarm and Evolutionary Computation

  • ISSN

    2210-6502

  • e-ISSN

  • Svazek periodika

    65

  • Číslo periodika v rámci svazku

    65

  • Stát vydavatele periodika

    US - Spojené státy americké

  • Počet stran výsledku

    18

  • Strana od-do

  • Kód UT WoS článku

    000680430000017

  • EID výsledku v databázi Scopus

    2-s2.0-85108854822