A two-stage dominance-based surrogate-assisted evolution algorithm for high-dimensional expensive multi-objective optimization

In the past decades, surrogate-assisted evolutionary algorithms (SAEAs) have become one of the most popular methods to solve expensive multi-objective optimization problems (EMOPs). However, most existing methods focus on low-dimensional EMOPs because a large number of training samples are required...

Full description

Saved in:
Bibliographic Details
Published in:Scientific reports 2023-08, Vol.13 (1), p.13163-13163, Article 13163
Main Authors: Yu, Mengjiao, Wang, Zheng, Dai, Rui, Chen, Zhongkui, Ye, Qianlin, Wang, Wanliang
Format: Article
Language:English
Subjects:
639/166
639/705
Algorithms
Approximation
Archives & records
Dominance
Efficiency
Genetic algorithms
Humanities and Social Sciences
Machine learning
multidisciplinary
Neural networks
Objective function
Optimization
Science
Science (multidisciplinary)
Support vector machines
Training
Variables
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In the past decades, surrogate-assisted evolutionary algorithms (SAEAs) have become one of the most popular methods to solve expensive multi-objective optimization problems (EMOPs). However, most existing methods focus on low-dimensional EMOPs because a large number of training samples are required to build accurate surrogate models, which is unrealistic for high-dimensional EMOPs. Therefore, this paper develops a two-stage dominance-based surrogate-assisted evolution algorithm (TSDEA) for high-dimensional EMOPs which utilizes the RBF model to approximate each objective function. First, a two-stage selection strategy is applied to select individuals for re-evaluation. Then considering the training time of the model, proposing a novel archive updating strategy to limit the number of individuals for updating. Experimental results show that the proposed algorithm has promising performance and computational efficiency compared to the state-of-the-art five SAEAs.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-40019-6