Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems

• URL: http://arxiv.org/abs/2212.06438v1
• Date: Tue, 13 Dec 2022 09:10:08 GMT
• Title: Multi-surrogate Assisted Efficient Global Optimization for Discrete Problems
• Authors: Qi Huang, Roy de Winter, Bas van Stein, Thomas B\"ack, Anna V. Kononova
• Abstract summary: This paper investigates the possible benefit of a concurrent utilization of multiple simulation-based surrogate models to solve discrete problems. Our findings indicate that SAMA-DiEGO can rapidly converge to better solutions on a majority of the test problems.
• Score: 0.9127162004615265
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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