A surrogate-assisted evolutionary algorithm based on problem reconstruction and feature extraction for high-dimensional expensive multi-objective optimization problems

doi:10.1016/j.asoc.2026.115673

A surrogate-assisted evolutionary algorithm based on problem reconstruction and feature extraction for high-dimensional expensive multi-objective optimization problems

2026-06-10

0

PRE

AI

DOI：10.1016/j.asoc.2026.115673

Original

Original request for help

Save

Abstract

En 中文

• Propose a two-stage surrogate-assisted algorithm for expensive optimization. • Propose problem reconstruction to reduce the dimensionality of decision variables. • Apply adaptive feature extraction to enhance the prediction accuracy of Kriging. • Achieve better non-dominated fronts on DTLZ, WFG and real-world problems.

A surrogate-assisted evolutionary algorithm based on problem reconstruction and feature extraction for high-dimensional expensive multi-objective optimization problems

Abstract

Journal

Organization