Opfunu: An Open-source Python Library for Optimization Benchmark Functions: papers linking to it

Zenodo · Software · 2024 · archives github.com/thieu1995/opfunu · doi.org/10.5281/zenodo.3620960

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to Opfunu: An Open-source Python Library for Optimization Benchmark Functions (https://doi.org/10.5281/zenodo.3620960), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

Key findings#

3 papers in the tracked journals link to the Zenodo dataset Opfunu: An Open-source Python Library for Optimization Benchmark Functions in their Crossref records, the first in 2022; most of them (2) in 2024.
By category: AI (3); the journals with the most are Neural Computing and Applications (2) and Knowledge and Information Systems (1).
2 of the papers build on one another: a later paper cites an earlier one that also links Opfunu: An Open-source Python Library for Optimization Benchmark Functions.

By year#

20222024

Papers linking to Opfunu: An Open-source Python Library for Optimization Benchmark Functions by publication year
Year	Papers
2022	1
2024	2

Journals#

Journals whose papers link to Opfunu: An Open-source Python Library for Optimization Benchmark Functions most
Journal	Papers
Neural Computing and Applications	2
Knowledge and Information Systems	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

SRIME: a strengthened RIME with Latin hypercube sampling and embedded distance-based selection for engineering optimization problems Neural Computing and Applications, 2024
- Cooperative coati optimization algorithm with transfer functions for feature selection and knapsack problems Knowledge and Information Systems, 2024 — cites the paper above

Papers linking to Opfunu: An Open-source Python Library for Optimization Benchmark Functions, most cited first
Published	Title	Journal	Authors	Citations	Link
2024-02-12	SRIME: a strengthened RIME with Latin hypercube sampling and embedded distance-based selection for engineering optimization problems	Neural Computing and Applications	Rui Zhong, Jun Yu, Chao Zhang et al.	71	cites it
2022-03-06	FP-SMA: an adaptive, fluctuant population strategy for slime mould algorithm	Neural Computing and Applications	Jassim Al-Fadhli, Ali Jaragh, Mohammad Gh. Alfailakawi et al.	18	cites it
2024-07-15	Cooperative coati optimization algorithm with transfer functions for feature selection and knapsack problems	Knowledge and Information Systems	Rui Zhong, Chao Zhang, Jun Yu	14	cites it

How this is measured#

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

Cite this page

Scholar4AI. (2026). Opfunu: An Open-source Python Library for Optimization Benchmark Functions: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/zenodo-3620960

@misc{scholar4ai-code-zenodo-3620960,
  author = {{Scholar4AI}},
  title = {Opfunu: An Open-source Python Library for Optimization Benchmark Functions: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/zenodo-3620960}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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