Harris Hawks Optimisation: Using of an Archive

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61988987%3A17310%2F21%3AA2202A0W" target="_blank" >RIV/61988987:17310/21:A2202A0W - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/record/display.uri?eid=2-s2.0-85117512842&origin=resultslist&sort=plf-f&featureToggles=FEATURE_NEW_DOC_DETAILS_EXPORT:1" target="_blank" >https://www.scopus.com/record/display.uri?eid=2-s2.0-85117512842&origin=resultslist&sort=plf-f&featureToggles=FEATURE_NEW_DOC_DETAILS_EXPORT:1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-030-87986-0_37" target="_blank" >10.1007/978-3-030-87986-0_37</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Harris Hawks Optimisation: Using of an Archive

Popis výsledku v původním jazyce

This paper proposes an enhanced variant of the novel and popular Harris Hawks Optimisation (HHO) method. The original HHO algorithm was studied in many research projects, and a lot of hybrid (cooperative) variants of HHO was proposed. In this research study, an advanced HHO algorithm with an archive of the old solutions is proposed (HHOA). The proposed method is experimentally compared with the original HHO algorithm on a set of 22 real-world problems (CEC 2011). The results illustrate the superiority of HHOA because it outperforms HHO significantly in 20 out of 22 problems, and it is never significantly worse. Four well-known nature-based algorithms were employed to compare the efficiency of the proposed algorithm. HHOA achieves the best results in overall statistical comparison. A more detailed comparison shows that HHOA achieves the best results in half real-world problems, and it is never the worst-performing method. A newly employed archive of old solutions significantly increases the performance of the original HHO algorithm.

Název v anglickém jazyce

Harris Hawks Optimisation: Using of an Archive

Popis výsledku anglicky

This paper proposes an enhanced variant of the novel and popular Harris Hawks Optimisation (HHO) method. The original HHO algorithm was studied in many research projects, and a lot of hybrid (cooperative) variants of HHO was proposed. In this research study, an advanced HHO algorithm with an archive of the old solutions is proposed (HHOA). The proposed method is experimentally compared with the original HHO algorithm on a set of 22 real-world problems (CEC 2011). The results illustrate the superiority of HHOA because it outperforms HHO significantly in 20 out of 22 problems, and it is never significantly worse. Four well-known nature-based algorithms were employed to compare the efficiency of the proposed algorithm. HHOA achieves the best results in overall statistical comparison. A more detailed comparison shows that HHOA achieves the best results in half real-world problems, and it is never the worst-performing method. A newly employed archive of old solutions significantly increases the performance of the original HHO algorithm.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

10102 - Applied mathematics

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2021

Kód důvěrnosti údajů

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

Název statě ve sborníku

Lecture Notes in Artificial Intelligence 12854

ISBN

978-303087985-3

ISSN

0302-9743

e-ISSN

—

Počet stran výsledku

9

Strana od-do

415-423

Název nakladatele

Springer

Místo vydání

Cham, Switzerland

Místo konání akce

Zakopane, Polsko

Datum konání akce

20. 6. 2021

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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