An activity level based surrogate-assisted evolutionary algorithm for many-objective optimization

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F24%3A10256809" target="_blank" >RIV/61989100:27240/24:10256809 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S1568494624007415?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S1568494624007415?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.asoc.2024.111967" target="_blank" >10.1016/j.asoc.2024.111967</a>

Result language

angličtina

Original language name

An activity level based surrogate-assisted evolutionary algorithm for many-objective optimization

Original language description

Addressing expensive many-objective optimization problems (MaOPs) is a formidable challenge owing to their intricate objective spaces and high computational demands. Surrogate-assisted evolutionary algorithms (SAEAs) have gained prominence because of their ability to tackle MaOPs efficiently. They achieve this by using surrogate models to approximate objective functions, significantly reducing their reliance on costly evaluations. However, the effectiveness of many SAEAs is hampered by their reliance on various surrogate models and optimization strategies, which often result in suboptimal prediction accuracy and optimization performance. This study introduces a novel approach: an activity level based surrogate-assisted reference vector guided evolutionary algorithm specifically designed for expensive MaOPs. Utilizing the Kriging model and an angle penalty distance criterion, this algorithm effectively filters solutions that require evaluation using the original function. It employs a fixed number of training sets,that are updated via a two-screening strategy that leverages activity levels to refine population screening. This process ensures that the reference vector progressively aligns more closely with the Pareto fronts,which is enhanced by the deployment of adjusted adaptive reference vectors, thereby improving the screening precision. The proposed algorithm was tested against six contemporary algorithms using the DTLZ, WFG, and MaF test suites. The experimental results show that the proposed method outperforms other algorithms in most problems. Furthermore, its application to the cloud computing task scheduling problem underscores its practical value, demonstrating its notable effectiveness. The experimental outcomes attest to the robust performance of the algorithm across both test scenarios and real-world applications.

Czech name

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Czech description

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Type

J_imp - 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

Project

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Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2024

Confidentiality

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

Name of the periodical

Applied Soft Computing

ISSN

1568-4946

e-ISSN

1872-9681

Volume of the periodical

164

Issue of the periodical within the volume

říjen 2024

Country of publishing house

US - UNITED STATES

Number of pages

18

Pages from-to

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UT code for WoS article

001271671100001

EID of the result in the Scopus database

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