Surrogate-assisted differential evolutionary algorithm with dynamic region exploration for expensive optimization problems
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10260370" target="_blank" >RIV/61989100:27240/25:10260370 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S1568494625009305?pes=vor&utm_source=clarivate&getft_integrator=clarivate" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1568494625009305?pes=vor&utm_source=clarivate&getft_integrator=clarivate</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.asoc.2025.113619" target="_blank" >10.1016/j.asoc.2025.113619</a>
Alternative languages
Result language
angličtina
Original language name
Surrogate-assisted differential evolutionary algorithm with dynamic region exploration for expensive optimization problems
Original language description
Currently, surrogate-assisted evolutionary algorithms (SAEAs) are widely used to solve computationally expensive optimization problems. However, in complex multimodal scenarios, there exists a notable likelihood that the algorithm might not converge to the optimal solution, highlighting the need to enhance the exploration capability of SAEAs. Therefore, exploring or developing different regions at different stages of the algorithm is crucial. This study designs a surrogate-assisted differential evolutionary algorithm with dynamic region exploration (DREDE) for expensive optimization problems. To filter out current suspected regions where better solutions might exist, anew criterion is proposed that combines the fitness level of a sample and its distance from the current optimal solution. By constructing surrogate models, DREDE dynamically performs global, local, and suspected region searches. A method for improving model accuracy by calculating the mean of selected individuals is introduced. These strategies synergistically enhance the performance of DREDE, which is comprehensively compared with several advanced SAEAs on seven benchmark functions with varying dimensions and was used in the reducer design problem. Simulation results showed that DREDE has a promising future in addressing costly practical issues.
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
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Applied Soft Computing
ISSN
1568-4946
e-ISSN
1872-9681
Volume of the periodical
183
Issue of the periodical within the volume
Nov
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
17
Pages from-to
nestránkováno
UT code for WoS article
001541799100006
EID of the result in the Scopus database
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