Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems

arXiv (Cornell University)

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.

Authors 5

1. Qi Huang Aachen

   Leiden University · University of Applied Sciences Leiden

   Affiliation as printed

   Leiden Institute of Advanced Computer Science Leiden University Leiden , Netherlands

2. Roy de Winter Aachen

   Leiden University · University of Applied Sciences Leiden

   Affiliation as printed

   Leiden Institute of Advanced Computer Science Leiden University Leiden , Netherlands

3. Bas van Stein Aachen

   Leiden University · University of Applied Sciences Leiden

   Affiliation as printed

   Leiden Institute of Advanced Computer Science Leiden University Leiden , Netherlands

4. Thomas Bäck
5. Anna V. Kononova Aachen

   Leiden University · University of Applied Sciences Leiden

   Affiliation as printed

   Leiden Institute of Advanced Computer Science Leiden University Leiden , Netherlands