Detail publikace

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.

Originální název

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]

Typ

článek v časopise ve Web of Science, Jimp

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

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

Autoři

KŮDELA, J.

Vydáno

18. 5. 2022

Nakladatel

Elsevier

ISSN

0952-1976

Periodikum

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE

Ročník

113

Číslo

1

Stát

Spojené království Velké Británie a Severního Irska

Strany od

1

Strany do

3

Strany počet

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"
}