High-dimensional normalized data profiles for testing derivative-free optimization algorithms

This article provides a new tool for examining the efficiency and robustness of derivative-free optimization algorithms based on high-dimensional normalized data profiles that test a variety of performance metrics. Unlike the traditional data profiles that examine a single dimension, the proposed da...

Descripción completa

Detalles Bibliográficos
Autores principales: Musafer, Hassan, Tokgoz, Emre, Mahmood, Ausif
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Algorithms and Analysis of Algorithms
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454945/
https://www.ncbi.nlm.nih.gov/pubmed/36091977
http://dx.doi.org/10.7717/peerj-cs.960

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9454945/
https://www.ncbi.nlm.nih.gov/pubmed/36091977
http://dx.doi.org/10.7717/peerj-cs.960

Ejemplares similares