Surrogate-Assisted Autoencoder-Embedded Evolutionary Optimization Algorithm to Solve High-Dimensional Expensive Problems

Document Type

Article

Publication Date

8-1-2022

Abstract

Surrogate-assisted evolutionary algorithms (EAs) have been intensively used to solve computationally expensive problems with some success. However, traditional EAs are not suitable to deal with high-dimensional expensive problems (HEPs) with high-dimensional search space even if their fitness evaluations are assisted by surrogate models. The recently proposed autoencoder-embedded evolutionary optimization (AEO) framework is highly appropriate to deal with high-dimensional problems. This work aims to incorporate surrogate models into it to further boost its performance, thus resulting in surrogate-assisted AEO (SAEO). It proposes a novel model management strategy that can guarantee reasonable amounts of re-evaluations; hence, the accuracy of surrogate models can be enhanced via being updated with new evaluated samples. Moreover, to ensure enough data samples before constructing surrogates, a problem-dimensionality-dependent activation condition is developed for incorporating surrogates into the SAEO framework. SAEO is tested on seven commonly used benchmark functions and compared with state-of-the-art algorithms for HEPs. The experimental results show that SAEO can further enhance the performance of AEO on most cases and SAEO performs significantly better than other algorithms. Therefore, SAEO has great potential to deal with HEPs.

Identifier

85115704634 (Scopus)

Publication Title

IEEE Transactions on Evolutionary Computation

External Full Text Location

https://doi.org/10.1109/TEVC.2021.3113923

e-ISSN

19410026

ISSN

1089778X

First Page

676

Last Page

689

Issue

4

Volume

26

Grant

2021SHZDZX0100

Fund Ref

National Natural Science Foundation of China

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