CSS-A Cheap-Surrogate-Based Selection Operator for Multi-objective Optimization
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F22%3A10252181" target="_blank" >RIV/61989100:27240/22:10252181 - isvavai.cz</a>
Result on the web
<a href="https://link.springer.com/chapter/10.1007/978-3-031-21094-5_5" target="_blank" >https://link.springer.com/chapter/10.1007/978-3-031-21094-5_5</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-031-21094-5_5" target="_blank" >10.1007/978-3-031-21094-5_5</a>
Alternative languages
Result language
angličtina
Original language name
CSS-A Cheap-Surrogate-Based Selection Operator for Multi-objective Optimization
Original language description
Due to the complex topology of the search space, expensive multi-objective evolutionary algorithms (EMOEAs) emphasize enhancing the exploration capability. Many algorithms use ensembles of surrogate models to boost the performance. Generally, the surrogate-based model either works out the solution's fitness by approximating the evaluation function or selects the solution by weighting the uncertainty degree of candidate solutions. This paper proposes a selection operator called Cheap surrogate selection (CSS) for multi-objective problems by utilizing the density probability on a k-dimensional tree. As opposed to the first type of surrogate models, which approximate the objective function, the proposed CSS only estimates the uncertainty of the candidate solutions. As a result, CSS does not require extensive sampling or training. Besides, CSS makes use of neighbors' density and builds the tree with low computational complexity, resulting in an accelerated surrogate process. Moreover, a new EMOEA is proposed by integrating spherical search as the core optimizer with the proposed selection scheme. Over a wide variety of benchmark problems, we show that the proposed method outperforms several state-of-the-art EMOEAs. (C) 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
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
10200 - Computer and information sciences
Result continuities
Project
<a href="/en/project/LTAIN19176" target="_blank" >LTAIN19176: Metaheuristics Framework for Multi-objective Combinatorial Optimization Problems (META MO-COP)</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Volume 13627
ISBN
978-3-031-21093-8
ISSN
0302-9743
e-ISSN
1611-3349
Number of pages
15
Pages from-to
54-68
Publisher name
Springer
Place of publication
Cham
Event location
Maribor
Event date
Nov 17, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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