Enhancing a hierarchical evolutionary strategy using the nearest-better clustering
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F24%3A63580358" target="_blank" >RIV/70883521:28140/24:63580358 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-63759-9_43" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-63759-9_43</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-63759-9_43" target="_blank" >10.1007/978-3-031-63759-9_43</a>
Alternative languages
Result language
angličtina
Original language name
Enhancing a hierarchical evolutionary strategy using the nearest-better clustering
Original language description
A straightforward way of solving global optimization problems is to find all local optima of the objective function. Therefore, the ability of detecting multiple local optima is a key feature of a practically usable global optimization method. One of such methods is a multi-population evolutionary strategy called the Hierarchic Memetic Strategy (HMS). Although HMS has already proven its global optimization capabilities there is an area for improvement. In this paper we show such an enhancement resulting from the application of the Nearest-Better Clustering. Results of experiments consisting both of curated benchmarks and a real-world inverse problem show that on average the performance is indeed improved compared to the baseline HMS and remains on par with state-of-the-art evolutionary global optimization methods.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
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
<a href="/en/project/GF21-45465L" target="_blank" >GF21-45465L: Metaheuristic-based parametric optimization of time-delay models and control systems</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Computational Science, ICCS 2024, pt III
ISBN
978-3-031-63758-2
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
15
Pages from-to
423-437
Publisher name
Springer International Publishing AG
Place of publication
Basel
Event location
Malaga
Event date
Jul 2, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001279325500043