Self-organizing migrating algorithm: review, improvements and comparison
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10249947" target="_blank" >RIV/61989100:27240/22:10249947 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/content/pdf/10.1007/s10462-022-10167-8.pdf" target="_blank" >https://link.springer.com/content/pdf/10.1007/s10462-022-10167-8.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10462-022-10167-8" target="_blank" >10.1007/s10462-022-10167-8</a>
Alternative languages
Result language
angličtina
Original language name
Self-organizing migrating algorithm: review, improvements and comparison
Original language description
The self-organizing migrating algorithm (SOMA) is a population-based meta-heuristic that belongs to swarm intelligence. In the last 20 years, we can observe two main streams in the publications. First, novel approaches contributing to the improvement of its performance. Second, solving the various optimization problems. Despite the different approaches and applications, there exists no work summarizing them. Therefore, this work reviews the research papers dealing with the principles and application of the SOMA. The second goal of this work is to provide additional information about the performance of the SOMA. This work presents the comparison of the selected algorithms. The experimental results indicate that the best-performing SOMAs provide competitive results comparing the recently published algorithms. (C) 2022, The Author(s).
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10200 - Computer and information sciences
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Artificial Intelligence Review
ISSN
0269-2821
e-ISSN
1573-7462
Volume of the periodical
Neuveden
Issue of the periodical within the volume
Duben 2022
Country of publishing house
US - UNITED STATES
Number of pages
72
Pages from-to
nestrankovano
UT code for WoS article
000778060000001
EID of the result in the Scopus database
2-s2.0-85127552404