Improving differential evolution algorithm by synergizing different improvement mechanisms

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F12%3A86092947" target="_blank" >RIV/61989100:27240/12:86092947 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1145/2240166.2240170" target="_blank" >http://dx.doi.org/10.1145/2240166.2240170</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1145/2240166.2240170" target="_blank" >10.1145/2240166.2240170</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Improving differential evolution algorithm by synergizing different improvement mechanisms

Popis výsledku v původním jazyce

Differential Evolution (DE) is a well-known Evolutionary Algorithm (EA) for solving global optimization problems. Practical experiences, however, show that DE is vulnerable to problems like slow and/ or premature convergence. In this article we propose asimple and modified DE framework, called MDE, which is a fusion of three recent modifications in DE: (1) Opposition-Based Learning (OBL); (2) tournament method for mutation; and (3) single population structure. These features have a specific role whichhelps in improving the performance of DE. While OBL helps in giving a good initial start to DE, the use of the tournament best base vector in the mutation phase helps in preserving the diversity. Finally the single population structure helps in faster convergence. Their synergized effect balances the exploitation and exploration capabilities of DE without compromising with the solution quality or the convergence rate. The proposed MDE is validated on a set of 25 standard benchmark proble

Název v anglickém jazyce

Improving differential evolution algorithm by synergizing different improvement mechanisms

Popis výsledku anglicky

Differential Evolution (DE) is a well-known Evolutionary Algorithm (EA) for solving global optimization problems. Practical experiences, however, show that DE is vulnerable to problems like slow and/ or premature convergence. In this article we propose asimple and modified DE framework, called MDE, which is a fusion of three recent modifications in DE: (1) Opposition-Based Learning (OBL); (2) tournament method for mutation; and (3) single population structure. These features have a specific role whichhelps in improving the performance of DE. While OBL helps in giving a good initial start to DE, the use of the tournament best base vector in the mutation phase helps in preserving the diversity. Finally the single population structure helps in faster convergence. Their synergized effect balances the exploitation and exploration capabilities of DE without compromising with the solution quality or the convergence rate. The proposed MDE is validated on a set of 25 standard benchmark proble

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

IN - Informatika

OECD FORD obor

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Projekt

<a href="/cs/project/GA201%2F09%2F0990" target="_blank" >GA201/09/0990: Zpracování XML dat</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2012

Kód důvěrnosti údajů

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

Název periodika

ACM Transactions on Autonomous and Adaptive Systems

ISSN

1556-4665

e-ISSN

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Svazek periodika

7

Číslo periodika v rámci svazku

2

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

32

Strana od-do

1-32

Kód UT WoS článku

000307171100004

EID výsledku v databázi Scopus

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