Publication detail

Novel Zigzag-based Benchmark Functions for Bound Constrained Single Objective Optimization

KŮDELA, J.

Original Title

Novel Zigzag-based Benchmark Functions for Bound Constrained Single Objective Optimization

Type

conference paper

Language

English

Original Abstract

The development and comparison of new optimization methods in general, and evolutionary algorithms in particular, rely heavily on benchmarking. In this paper, the construction of novel zigzag-based benchmark functions for bound constrained single objective optimization is presented. The new benchmark functions are non-differentiable, highly multimodal, and have a built-in parameter that controls the complexity of the function. To investigate the properties of the new benchmark functions two of the best algorithms from the CEC'20 Competition on Single Objective Bound Constrained Optimization, as well as one standard evolutionary algorithm, were utilized in a computational study. The results of the study suggest that the new benchmark functions are very well suited for algorithmic comparison.

Keywords

benchmark functions; single objective optimization; zigzag function

Authors

KŮDELA, J.

Released

1. 7. 2021

Publisher

IEEE

ISBN

978-1-7281-8393-0

Book

2021 IEEE Congress on Evolutionary Computation (CEC)

Pages from

857

Pages to

862

Pages count

6

URL

https://ieeexplore.ieee.org/document/9504720

BibTex

@inproceedings{BUT175647,
  author="Jakub {Kůdela}",
  title="Novel Zigzag-based Benchmark Functions for Bound Constrained Single Objective Optimization",
  booktitle="2021 IEEE Congress on Evolutionary Computation (CEC)",
  year="2021",
  pages="857--862",
  publisher="IEEE",
  doi="10.1109/CEC45853.2021.9504720",
  isbn="978-1-7281-8393-0",
  url="https://ieeexplore.ieee.org/document/9504720"
}