Publication detail

Commentary on: “STOA: A bio-inspired based optimization algorithm for industrial engineering problems” [EAAI, 82 (2019), 148–174] and “Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization” [EAAI, 90 (2020), no. 103541]

KŮDELA, J.

Original Title

Commentary on: “STOA: A bio-inspired based optimization algorithm for industrial engineering problems” [EAAI, 82 (2019), 148–174] and “Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization” [EAAI, 90 (2020), no. 103541]

Type

journal article in Web of Science

Language

English

Original Abstract

This commentary concerns two recently developed metaheuristic algorithms, namely the Sooty Tern Optimization Algorithm, and the Tunicate Swarm Algorithm. Both of these algorithms claim computational superiority over other methods based on experimental results on a certain benchmark set. The aim of this note is to aware researchers that this claim is not valid: the proposed algorithms use a zero-bias operator and many of the studied benchmark functions on which they were found superior have optimal solutions located in the zero vector. Moreover, the codes for the methods provided by the authors are not achieving the results reported in the respective publications.

Keywords

Sooty Tern Optimization; Tunicate Swarm Algorithm; Metaheuristic optimization; Benchmarking

Authors

KŮDELA, J.

Released

18. 5. 2022

Publisher

Elsevier

ISBN

0952-1976

Periodical

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Year of study

113

Number

1

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

3

Pages count

3

URL

https://www.sciencedirect.com/science/article/pii/S095219762200149X

BibTex

@article{BUT178347,
  author="Jakub {Kůdela}",
  title="Commentary on: “STOA: A bio-inspired based optimization algorithm for industrial engineering problems” [EAAI, 82 (2019), 148–174] and “Tunicate Swarm Algorithm: A new bio-inspired based metaheuristic paradigm for global optimization” [EAAI, 90 (2020), no. 103541]",
  journal="ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE",
  year="2022",
  volume="113",
  number="1",
  pages="1--3",
  doi="10.1016/j.engappai.2022.104930",
  issn="0952-1976",
  url="https://www.sciencedirect.com/science/article/pii/S095219762200149X"
}