Benchmarking Derivative-Free Global Optimization Algorithms Under Limited Dimensions and Large Evaluation Budgets

This article addresses the challenge of selecting the most suitable optimization algorithm by presenting a comprehensive computational comparison between stochastic and deterministic methods. The complexity of algorithm selection arises from the absence of a universal algorithm and the abundance of...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE transactions on evolutionary computation Jg. 29; H. 1; S. 187 - 204
Hauptverfasser:	Stripinis, Linas, Kudela, Jakub, Paulavicius, Remigijus
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	IEEE 01.02.2025
Schlagworte:	Benchmark testing Closed box Derivative-free global optimization deterministic algorithms Evolutionary computation evolutionary computation (EC) algorithms Linear programming nature-inspired meta-heuristics numerical benchmarking Optimization Partitioning algorithms Stochastic processes
ISSN:	1089-778X, 1941-0026
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!