Improving differential evolution algorithm by synergizing different improvement mechanisms
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Improving differential evolution algorithm by synergizing different improvement mechanisms
Original language description
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
Czech name
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Czech description
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Classification
Type
J<sub>x</sub> - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)
CEP classification
IN - Informatics
OECD FORD branch
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Result continuities
Project
<a href="/en/project/GA201%2F09%2F0990" target="_blank" >GA201/09/0990: XML data processing</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2012
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
ACM Transactions on Autonomous and Adaptive Systems
ISSN
1556-4665
e-ISSN
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Volume of the periodical
7
Issue of the periodical within the volume
2
Country of publishing house
US - UNITED STATES
Number of pages
32
Pages from-to
1-32
UT code for WoS article
000307171100004
EID of the result in the Scopus database
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