Comparing the Brain Storm Optimization Algorithm on the Ambiguous Benchmark Set
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26210%2F22%3APU144875" target="_blank" >RIV/00216305:26210/22:PU144875 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-09677-8_31" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-09677-8_31</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-09677-8_31" target="_blank" >10.1007/978-3-031-09677-8_31</a>
Alternative languages
Result language
angličtina
Original language name
Comparing the Brain Storm Optimization Algorithm on the Ambiguous Benchmark Set
Original language description
In the field of evolutionary computation, benchmarking has a pivotal place in both the development of novel algorithms, and in performing comparisons between existing techniques. In this paper, the computational comparison of the Brain Storm Optimization (BSO) algorithm (a swarm intelligence paradigm inspired by the behaviors of the human process of brainstorming) was performed. A selected representative of the BSO algorithms (namely, BSO20) was compared with other selected methods, which were a mix of canonical methods (both swarm intelligence and evolutionary algorithms) and state-of-the-art techniques. As a test bed, the ambiguous benchmark set was employed. The results showed that even though BSO is not among the best algorithms on this test bed, it is still a well performing method comparable to some state-of-the-art algorithms.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
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
Book/collection name
Advances in Swarm Intelligence. ICSI 2022, Part II. Lecture Notes in Computer Science, vol 13344.
ISBN
978-3-031-09726-3
Number of pages of the result
13
Pages from-to
367-379
Number of pages of the book
548
Publisher name
Springer, Cham
Place of publication
Neuveden
UT code for WoS chapter
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