Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems

TL;DR

This paper introduces SAMA-DiEGO, a novel multi-surrogate optimization algorithm that effectively solves complex discrete problems by leveraging multiple surrogate models, demonstrating faster convergence and better solutions than existing methods.

Contribution

The paper proposes SAMA-DiEGO, a new multi-surrogate optimization method with a two-stage online model management strategy for discrete problems, outperforming single surrogate and non-surrogate algorithms.

Findings

SAMA-DiEGO converges faster to better solutions on most test problems.

Using multiple surrogate models enhances optimization performance for discrete problems.

The approach is effective on both binary-encoded and ordinal combinatorial problems.

Abstract

Decades of progress in simulation-based surrogate-assisted optimization and unprecedented growth in computational power have enabled researchers and practitioners to optimize previously intractable complex engineering problems. This paper investigates the possible benefit of a concurrent utilization of multiple simulation-based surrogate models to solve complex discrete optimization problems. To fulfill this, the so-called Self-Adaptive Multi-surrogate Assisted Efficient Global Optimization algorithm (SAMA-DiEGO), which features a two-stage online model management strategy, is proposed and further benchmarked on fifteen binary-encoded combinatorial and fifteen ordinal problems against several state-of-the-art non-surrogate or single surrogate assisted optimization algorithms. Our findings indicate that SAMA-DiEGO can rapidly converge to better solutions on a majority of the test problems, which shows the feasibility and advantage of using multiple surrogate models in optimizing discrete problems.