Evolutionary Multiobjective Optimization Assisted by Scalarization
Function Approximation for High-Dimensional Expensive Problems
Abstract
Surrogate-assisted evolutionary algorithms (SAEAs) are a promising
approach for solving expensive multiobjective optimization problems, but
they often cannot address high-dimensional problems. Although one common
approach to this challenge is to construct reliable surrogates, their
accuracy inevitably deteriorates in a high-dimensional search space.
Thus, this paper presents a novel SAEA based on scalarization function
approximation, which is designed to strengthen its robustness against
this deterioration. The proposed algorithm constructs an approximation
model for each scalarization function defined in a decomposition-based
framework. Each decomposed problem is then solved using multiple
independent models trained for its neighbor problems. The intent is to
decrease the risk of search performance degradations caused by
unreliable approximations and retain the redundancy of the
surrogate-assisted search to hedge the risk of over-fitting.
Furthermore, each approximation model is adapted to a promising region
of its corresponding decomposed problem to reduce the complexity of
model fitting given a limited number of training samples. Experimental
results show that the proposed algorithm is competitive with
state-of-the-art SAEAs adapted for high-dimensional problems.