Efficient three-stage surrogate-assisted differential evolution for expensive optimization problems

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10259469" target="_blank" >RIV/61989100:27240/25:10259469 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/61989100:27730/25:10259469

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2210650225002512" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2210650225002512</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.swevo.2025.102093" target="_blank" >10.1016/j.swevo.2025.102093</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Efficient three-stage surrogate-assisted differential evolution for expensive optimization problems

Popis výsledku v původním jazyce

Surrogate-assisted evolutionary algorithms (SAEAs) have received significant acclaim for dealing with intricate and computationally demanding optimization problems. However, a prevalent challenge in many existing algorithms lies in their relatively sluggish convergence in the later stages of optimization. This study introduces an innovative three-stage surrogate-assisted differential evolution (DE) approach that adeptly addresses the demands of early exploration and subsequent exploitation by employing distinct mutation operators and surrogate models. In the initial stage, a combination of radial basis function and multi-dimensional Lipschitz function-assisted DE efficiently identifies a promising region within the complete decision space. After that, the subsequent stage employs a hybrid approach of local and global surrogate-assisted DE, renowned for its robust exploitation capabilities, to hasten the optimization process. This stage incorporates an on-the-fly update approach for both populations and surrogate models, utilizing a pre-set quantity of top-ranked individuals to facilitate updates. Additionally, a sampling technique based on a full-mutation operator is employed to incorporate the best genotypes in the population effectively. A surrogate-assisted local search operator is leveraged in the final stage to optimize the ultimate solution. This stage integrates a radial basis function-based local surrogate function and an interior-point method, enhancing sampling efficiency within the designated local region of interest. The efficacy of the three-stage framework and the proposed strategy is thoroughly validated through simulation experiments, empirical analyses, and ablation studies. Furthermore, we compare the proposed algorithm against other state-of-the-art surrogate-assisted evolutionary algorithms (SAEAs) on a diverse set of expensive benchmark functions and a real-world problem, demonstrating superior performance in terms of both robustness and effectiveness.

Název v anglickém jazyce

Efficient three-stage surrogate-assisted differential evolution for expensive optimization problems

Popis výsledku anglicky

Surrogate-assisted evolutionary algorithms (SAEAs) have received significant acclaim for dealing with intricate and computationally demanding optimization problems. However, a prevalent challenge in many existing algorithms lies in their relatively sluggish convergence in the later stages of optimization. This study introduces an innovative three-stage surrogate-assisted differential evolution (DE) approach that adeptly addresses the demands of early exploration and subsequent exploitation by employing distinct mutation operators and surrogate models. In the initial stage, a combination of radial basis function and multi-dimensional Lipschitz function-assisted DE efficiently identifies a promising region within the complete decision space. After that, the subsequent stage employs a hybrid approach of local and global surrogate-assisted DE, renowned for its robust exploitation capabilities, to hasten the optimization process. This stage incorporates an on-the-fly update approach for both populations and surrogate models, utilizing a pre-set quantity of top-ranked individuals to facilitate updates. Additionally, a sampling technique based on a full-mutation operator is employed to incorporate the best genotypes in the population effectively. A surrogate-assisted local search operator is leveraged in the final stage to optimize the ultimate solution. This stage integrates a radial basis function-based local surrogate function and an interior-point method, enhancing sampling efficiency within the designated local region of interest. The efficacy of the three-stage framework and the proposed strategy is thoroughly validated through simulation experiments, empirical analyses, and ablation studies. Furthermore, we compare the proposed algorithm against other state-of-the-art surrogate-assisted evolutionary algorithms (SAEAs) on a diverse set of expensive benchmark functions and a real-world problem, demonstrating superior performance in terms of both robustness and effectiveness.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

10200 - Computer and information sciences

Projekt

—

Návaznosti

O - Projekt operacniho programu

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Swarm and Evolutionary Computation

ISSN

2210-6502

e-ISSN

2210-6510

Svazek periodika

98

Číslo periodika v rámci svazku

Volume 98

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

27

Strana od-do

nestránkováno

Kód UT WoS článku

001559858700001

EID výsledku v databázi Scopus

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