Publication detail

Comparing the Brain Storm Optimization Algorithm on the Ambiguous Benchmark Set

KŮDELA, J. NEVORAL, T. HOLOUBEK, T.

Original Title

Comparing the Brain Storm Optimization Algorithm on the Ambiguous Benchmark Set

Type

book chapter

Language

English

Original Abstract

In the field of evolutionary computation, benchmarking has a pivotal place in both the development of novel algorithms, and in performing comparisons between existing techniques. In this paper, the computational comparison of the Brain Storm Optimization (BSO) algorithm (a swarm intelligence paradigm inspired by the behaviors of the human process of brainstorming) was performed. A selected representative of the BSO algorithms (namely, BSO20) was compared with other selected methods, which were a mix of canonical methods (both swarm intelligence and evolutionary algorithms) and state-of-the-art techniques. As a test bed, the ambiguous benchmark set was employed. The results showed that even though BSO is not among the best algorithms on this test bed, it is still a well performing method comparable to some state-of-the-art algorithms.

Keywords

Brain Storm Optimization; Ambiguous benchmark set; Benchmarking; Numerical optimization; Single objective problems

Authors

KŮDELA, J.; NEVORAL, T.; HOLOUBEK, T.

Released

26. 6. 2022

Publisher

Springer, Cham

ISBN

978-3-031-09726-3

Book

Advances in Swarm Intelligence. ICSI 2022, Part II. Lecture Notes in Computer Science, vol 13344.

Pages from

367

Pages to

379

Pages count

13

URL

https://link.springer.com/chapter/10.1007/978-3-031-09677-8_31

BibTex

@inbook{BUT178338,
  author="Jakub {Kůdela} and Tomáš {Nevoral} and Tomáš {Holoubek}",
  title="Comparing the Brain Storm Optimization Algorithm on the Ambiguous Benchmark Set",
  booktitle="Advances in Swarm Intelligence. ICSI 2022, Part II. Lecture Notes in Computer Science, vol 13344.",
  year="2022",
  publisher="Springer, Cham",
  pages="367--379",
  doi="10.1007/978-3-031-09677-8\{_}31",
  isbn="978-3-031-09726-3",
  url="https://link.springer.com/chapter/10.1007/978-3-031-09677-8_31"
}