The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
PySwarms: a research toolkit for Particle Swarm Optimization in Python
Open accessAbstract
Particle swarm optimization (PSO) is a heuristic search technique that iteratively improves a set of candidate solutions given an objective measure of fitness (Kennedy and Eberhart 1995b). Although vanilla implementations of PSO can be found in some Python evolutionary algorithm toolboxes (Fortin et al. 2012; Biscani, Izzo, and Märtens 2017), a PSO-specific library that focuses on the said technique is still an open challenge.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
Cited by
Vaccine optimization for COVID-19: Who to vaccinate first?
Matrajt, L., Eaton, J., Leung, T., & Brown, E. R. (2021) · Science Advances · doi:10.1126/sciadv.abf1374 · Cited by 419
A generative modeling approach for benchmarking and training shallow quantum circuits
Benedetti, M., Garcia-Pintos, D., Perdomo, O., Leyton-Ortega, V., Nam, Y., & Perdomo-Ortiz, A. (2019) · npj Quantum Information · doi:10.1038/s41534-019-0157-8 · Cited by 379
Phoenics: A Bayesian Optimizer for Chemistry
Häse, F., Roch, L. M., Kreisbeck, C., & Aspuru-Guzik, A. (2018) · ACS Central Science · doi:10.1021/acscentsci.8b00307 · Cited by 379
Dynamic characterization of timber floor subassemblies: Sensitivity analysis and modeling issues
Pasca, D. P., Aloisio, A., Fragiacomo, M., & Tomasi, R. (2021) · Journal of Structural Engineering · doi:10.1061/(asce)st.1943-541x.0003179 · Cited by 15
Metadata
- Authors
- James V. Miranda, L.
- Venue
- The Journal of Open Source Software
- Publisher
- The Open Journal
- Published
- 2018-01-10
- Volume
- 3
- Issue
- 21
- Pages
- 433
- ISSN
- 2475-9066 (ISSN-L)
- OpenAlex
- W2783515600
- Cited by
- 343
- References
- 1
- Type
- software paper
- Language
- en
- Open access
- diamond
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: PySwarms: a research toolkit for Particle Swarm Optimization in Python. https://app.cochat.ai/papers/10.21105/joss.00433
- The paper
- James V. Miranda, L. (2018). PySwarms: a research toolkit for Particle Swarm Optimization in Python. The Journal of Open Source Software. https://doi.org/10.21105/joss.00433
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
