Regression and relation-assisted evolutionary algorithm for high-dimensional expensive multi-objective optimization

Surrogate-assisted evolutionary algorithms (SAEAs) have gained a lot of attention to handle expensive multi-objective optimization problems (EMOPs). However, when it comes to high-dimensional EMOPs (HEMOPs), the performance of existing SAEAs degrades dramatically because of the dimensionality sensit...

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Veröffentlicht in:Swarm and evolutionary computation Jg. 97; S. 101978
Hauptverfasser: Zhu, Shuwei, Zhang, Yimo, Fang, Wei, Cui, Meiji, Deb, Kalyanmoy
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Elsevier B.V 01.08.2025
Schlagworte:
Evolutionary multi-objective optimization
Expensive optimization problems
High-dimensional optimization
Infill sampling criterion
Surrogate-assisted optimization
Expensive optimization problems
Surrogate-assisted optimization
High-dimensional optimization
Evolutionary multi-objective optimization
Infill sampling criterion
ISSN:2210-6502
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Zusammenfassung:Surrogate-assisted evolutionary algorithms (SAEAs) have gained a lot of attention to handle expensive multi-objective optimization problems (EMOPs). However, when it comes to high-dimensional EMOPs (HEMOPs), the performance of existing SAEAs degrades dramatically because of the dimensionality sensitivity issue, in which effective surrogate models are difficult to build. To this end, we propose a regression- and relation-assisted evolutionary algorithm (R2AEA) to deal with HEMOPs, which involves a regression-assisted weight optimization (RWO) stage and a relation-assisted multi-objective optimization (RMO) stage. To be specific, the RWO is facilitated by the problem transformation strategy and regression models. It reformulates the high-dimensional problem into a relative low-dimensional one and intends to converge to the Pareto-optimal front (PF) efficiently. Thereafter, the RMO concentrates on maintaining the population diversity with a new infill sampling criterion, which considers the optimization performance as well as the uncertainty estimated by the predicted entropy. To validate its effectiveness, we compare R2AEA with five state-of-the-art algorithms on various benchmark test suites with dimensions varying from 50 to 200, and six real-world HEMOPs. Experimental results show the superiority of R2AEA in terms of convergence speed and diversity maintenance with limited computational resources.
ISSN:2210-6502
DOI:10.1016/j.swevo.2025.101978