PySwarms: a research toolkit for Particle Swarm Optimization in Python
By Lester James Validad Miranda. Journal of Open Source Software, 2018
330 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence
View Paper: https://www.semanticscholar.org/paper/ac785945934eb408c4a482306e9382c13f6341ca
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In Artificial Intelligence
- Alejandro Perdomo-Ortiz
- Delfina Garcia-Pintos
- Oscar J. Perdomo
- Y. Nam
- Vicente Leyton-Ortega
- Marcello Benedetti
Citations in the corpus
- A generative modeling approach for benchmarking and training shallow quantum circuits (2018, methodology): “We use a global-best particle swarm optimization al-gorithm [41] implemented in the PySwarms [63] Python library.”