A pairwise comparison based surrogate-assisted evolutionary algorithm for expensive multi-objective optimization

Multi-objective optimization problems in many real-world applications are characterized by computationally or economically expensive objectives, which cannot provide sufficient function evaluations for evolutionary algorithms to converge. Thus, a variety of surrogate models have been employed to pro...

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Bibliographic Details
Published in:Swarm and evolutionary computation Vol. 80; p. 101323
Main Authors: Tian, Ye, Hu, Jiaxing, He, Cheng, Ma, Haiping, Zhang, Limiao, Zhang, Xingyi
Format: Journal Article
Language:English
Published: Elsevier B.V 01.07.2023
Subjects:
Evolutionary algorithms
Expensive multi-objective optimization
Pairwise comparison
Surrogate-assisted optimization
Evolutionary algorithms
Surrogate-assisted optimization
Pairwise comparison
Expensive multi-objective optimization
ISSN:2210-6502
Online Access:Get full text
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Summary:Multi-objective optimization problems in many real-world applications are characterized by computationally or economically expensive objectives, which cannot provide sufficient function evaluations for evolutionary algorithms to converge. Thus, a variety of surrogate models have been employed to provide much more virtual evaluations. Most existing surrogate models are essentially regressors or classifiers, which may suffer from low reliability in the approximation of complex objectives. In this paper, we propose a novel surrogate-assisted evolutionary algorithm, which employs a surrogate model to conduct pairwise comparisons between candidate solutions, rather than directly predicting solutions’ fitness values. In comparison to regression and classification models, the proposed pairwise comparison based model can better balance between positive and negative samples, and may be directly used, reversely used, or ignored according to its reliability in model management. As demonstrated by the experimental results on abundant benchmark and real-world problems, the proposed surrogate model is more accurate than popular surrogate models, leading to performance superiority over state-of-the-art surrogate models.
ISSN:2210-6502
DOI:10.1016/j.swevo.2023.101323