Ensemble centralities based Adaptive Artificial Bee algorithm

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F15%3A86096658" target="_blank" >RIV/61989100:27240/15:86096658 - isvavai.cz</a>

Výsledek na webu

<a href="http://ieeexplore.ieee.org/xpl/abstractKeywords.jsp?arnumber=7257312" target="_blank" >http://ieeexplore.ieee.org/xpl/abstractKeywords.jsp?arnumber=7257312</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Ensemble centralities based Adaptive Artificial Bee algorithm

Popis výsledku v původním jazyce

An adaptive Artificial Bee Colony algorithm based on centralities is presented in this paper. As complex networks are generated in evolutionary algorithms during iterations, it becomes possible to obtain meaningful information regarding population dynamics during evaluations. The three centralities of Degree, Closeness and Betweenness are used for adaptive population control of the algorithm, where population interaction is measured and least performing solutions are replaced. Two adaptive variants of the algorithm are presented, one based on a single population and the other on an ensemble population approach. The experimentation is conducted on various standard test functions, showing that the adaptive approaches offer an improvement upon the canonical algorithm.

Název v anglickém jazyce

Ensemble centralities based Adaptive Artificial Bee algorithm

Popis výsledku anglicky

An adaptive Artificial Bee Colony algorithm based on centralities is presented in this paper. As complex networks are generated in evolutionary algorithms during iterations, it becomes possible to obtain meaningful information regarding population dynamics during evaluations. The three centralities of Degree, Closeness and Betweenness are used for adaptive population control of the algorithm, where population interaction is measured and least performing solutions are replaced. Two adaptive variants of the algorithm are presented, one based on a single population and the other on an ensemble population approach. The experimentation is conducted on various standard test functions, showing that the adaptive approaches offer an improvement upon the canonical algorithm.

Druh

D - Stať ve sborníku

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA15-06700S" target="_blank" >GA15-06700S: Nekonvenční řízení komplexních systémů</a><br>

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2015

Kód důvěrnosti údajů

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

Název statě ve sborníku

2015 IEEE Congress on Evolutionary Computation (CEC) : proceedings : May 25-28, 2015, Sendai, Japan

ISBN

978-1-4799-7492-4

ISSN

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e-ISSN

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Počet stran výsledku

7

Strana od-do

3370-3376

Název nakladatele

IEEE

Místo vydání

Danvers

Místo konání akce

Sendai

Datum konání akce

25. 5. 2015

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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