Publication result detail

Comparing Surrogate-Assisted Evolutionary Algorithms on Optimization of a Simulation Model for Resource Planning Task for Hospitals

KŮDELA, J.; DOBROVSKÝ, L.; SHEHADEH, M.; HŮLKA, T.; MATOUŠEK, R.

Original Title

Comparing Surrogate-Assisted Evolutionary Algorithms on Optimization of a Simulation Model for Resource Planning Task for Hospitals

English Title

Comparing Surrogate-Assisted Evolutionary Algorithms on Optimization of a Simulation Model for Resource Planning Task for Hospitals

Type

Paper in proceedings (conference paper)

Original Abstract

Surrogate-assisted evolutionary algorithms (SAEAs) are currently among the most widely researched techniques for their capability to solve expensive real-world optimization problems. The development of these techniques and their bench-marking with other methods still relies almost exclusively on artificially created problems. In this paper, we use a real-world problem of optimizing the parameters of a hospital resource planning tool to compare the performance of nine state-of-the-art single-objective SAEAs. We find that there are significant differences between the performance of the compared methods on the selected instances, making the problems suitable for benchmarking SAEAs.

English abstract

Surrogate-assisted evolutionary algorithms (SAEAs) are currently among the most widely researched techniques for their capability to solve expensive real-world optimization problems. The development of these techniques and their bench-marking with other methods still relies almost exclusively on artificially created problems. In this paper, we use a real-world problem of optimizing the parameters of a hospital resource planning tool to compare the performance of nine state-of-the-art single-objective SAEAs. We find that there are significant differences between the performance of the compared methods on the selected instances, making the problems suitable for benchmarking SAEAs.

Keywords

Expensive optimization; evolutionary algorithm; surrogate model; resource planning; benchmarking; healthcare

Key words in English

Expensive optimization; evolutionary algorithm; surrogate model; resource planning; benchmarking; healthcare

Authors

KŮDELA, J.; DOBROVSKÝ, L.; SHEHADEH, M.; HŮLKA, T.; MATOUŠEK, R.

RIV year

2025

Released

08.08.2024

Publisher

IEEE

ISBN

979-8-3503-0836-5

Book

2024 IEEE Congress on Evolutionary Computation (CEC)

Pages count

8

URL

https://ieeexplore.ieee.org/document/10611951

BibTex

@inproceedings{BUT196903,
  author="Jakub {Kůdela} and Ladislav {Dobrovský} and Mhd Ali {Shehadeh} and Tomáš {Hůlka} and Radomil {Matoušek}",
  title="Comparing Surrogate-Assisted Evolutionary Algorithms on Optimization of a Simulation Model for Resource Planning Task for Hospitals",
  booktitle="2024 IEEE Congress on Evolutionary Computation (CEC)",
  year="2024",
  pages="8",
  publisher="IEEE",
  doi="10.1109/CEC60901.2024.10611951",
  isbn="979-8-3503-0836-5",
  url="https://ieeexplore.ieee.org/document/10611951"
}