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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.

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Author:Philipp Zmijewski, Nicolas Meseth
Title (English):SimBO - a framework for simulation-based optimization using bayesian optimization
Parent Title (English):Communications of the ECMS
Document Type:Conference Proceeding
Year of Completion:2023
Release Date:2024/01/11
Tag:Bayesian Optimization; Simulation-based Optimization
Page Number:7
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