SimBO - a framework for simulation-based optimization using bayesian optimization
- SimBO is a flexible framework for optimizing discrete event-driven simulations (DES) using sequential optimization algorithms. While specifically designed for Bayesian Optimization (BO) in the context of DES, SimBO can be applied to any black-box problem with other optimization algorithms. The framework consists of four encapsulated components - the black-box problem, the sequential optimization algorithm, a database for experiment configuration and results, and a web-based graphical user interface - that communicate via well-defined interfaces. Each component can be run in different environments, allowing for cooperation between different hardware- and software configurations. In our research context, SimBO’s architecture enabled BO algorithms to be run on a high-performance cluster with GPU support, while the simulation is executed on a local Windows machine using the Simio simulation software. The framework’s flexibility also makes it suitable for evolving from a research-focused tool to a production-ready, cloud-based optimization tool for modern algorithms.
Author: | Philipp Zmijewski, Nicolas Meseth |
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Title (English): | SimBO - a framework for simulation-based optimization using bayesian optimization |
URN: | urn:nbn:de:bsz:959-opus-52193 |
URL: | https://scs-europe.net/conf/ecms2023/ecms2023-accepted-papers/ |
Parent Title (English): | Communications of the ECMS |
Document Type: | Conference Proceeding |
Language: | English |
Year of Completion: | 2023 |
Release Date: | 2024/01/11 |
Tag: | Bayesian Optimization; Simulation-based Optimization |
Volume: | 37 |
Issue: | 1 |
Page Number: | 7 |
Note: | 37th ECMS International Conference on Modelling and Simulation, June 20-23. 2023, Florence (Italy) |
Faculties: | Fakultät AuL |
DDC classes: | 000 Allgemeines, Informatik, Informationswissenschaft / 004 Informatik |
Review Status: | Veröffentlichte Fassung/Verlagsversion |