High-Dimensional Expensive Optimization by Classification-based
Multiobjective Evolutionary Algorithm with Dimensionality Reduction
Abstract
Surrogate-assisted multiobjective evolutionary algorithms (SAMOEAs) are
a promising approach for solving expensive multiobjective optimization
problems (EMOPs), wherein the number of function evaluations is
extremely restricted due to expensive-to-evaluate objective functions.
However, most SAEAs are not well-scaled to high-dimensional problems
because the accuracy of surrogate models degrades as the problem
dimension increases. This paper proposes a dimensionality
reduction-based SAEA, which involves the following two strategies to
address high-dimensional EMOPs. First, mapping high-dimensional training
samples to a low dimensional space in building surrogate models can
boost the accuracy of surrogate models. Second, compared to
approximation-based surrogate models, reliable classification-based
models can be obtained under a few training samples. Accordingly, the
proposed algorithm is designed to integrate a dimensionality reduction
technique into an existing classification-based SAEA, MCEA/D. It builds
classification models in low-dimensional spaces and then utilizes these
models to estimate good solutions without expensive function
evaluations. Experimental results statistically confirm that the
proposed algorithm derives state of-the-art performance in many
experimental cases.