Publication 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.

English title

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

Type

conference paper

Language

en

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 in English

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

Released

08.08.2024

Publisher

IEEE

ISBN

979-8-3503-0836-5

Book

2024 IEEE Congress on Evolutionary Computation (CEC)

Pages count

8

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",
  month="August",
  publisher="IEEE",
  isbn="979-8-3503-0836-5"
}