Fast parallel PSO library for Python with support for CPU and GPU multithreading.
Project description
# fastPSO
[![Build Status](https://travis-ci.org/pribalta/fastPSO.svg?branch=master)](https://travis-ci.org/pribalta/fastPSO)
Fast parallel Particle Swarm Optimization package for Python
__fastPSO__ is an open source software library for Particle Swarm Optimization built with two goals in mind:
* Speed
* Parallelism
Its flexible architecture enables you to define complex objective functions, and to perform optimization in a __serial__ or __parallel__ setting. In addition, it offers detailed insights on the optimization process, helping practitioners profile their results.
## Installation
__pip__ __package__
```
pip install fastpso
```
### Requirements
* numpy
## Getting started
tbd
## License
__fastPSO__ is available under *MIT License*
If you plan on using this software for scientific purposes, please cite our work:
```
@inproceedings{lorenzo2017particle,
title={Particle swarm optimization for hyper-parameter selection in deep neural networks},
author={Lorenzo, Pablo Ribalta et al.},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
pages={481--488},
year={2017},
organization={ACM}
}
```
```
@inproceedings{lorenzo2017hyper,
title={Hyper-parameter selection in deep neural networks using parallel particle swarm optimization},
author={Lorenzo, Pablo Ribalta et al.},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion},
pages={1864--1871},
year={2017},
organization={ACM}
}
```
[![Build Status](https://travis-ci.org/pribalta/fastPSO.svg?branch=master)](https://travis-ci.org/pribalta/fastPSO)
Fast parallel Particle Swarm Optimization package for Python
__fastPSO__ is an open source software library for Particle Swarm Optimization built with two goals in mind:
* Speed
* Parallelism
Its flexible architecture enables you to define complex objective functions, and to perform optimization in a __serial__ or __parallel__ setting. In addition, it offers detailed insights on the optimization process, helping practitioners profile their results.
## Installation
__pip__ __package__
```
pip install fastpso
```
### Requirements
* numpy
## Getting started
tbd
## License
__fastPSO__ is available under *MIT License*
If you plan on using this software for scientific purposes, please cite our work:
```
@inproceedings{lorenzo2017particle,
title={Particle swarm optimization for hyper-parameter selection in deep neural networks},
author={Lorenzo, Pablo Ribalta et al.},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference},
pages={481--488},
year={2017},
organization={ACM}
}
```
```
@inproceedings{lorenzo2017hyper,
title={Hyper-parameter selection in deep neural networks using parallel particle swarm optimization},
author={Lorenzo, Pablo Ribalta et al.},
booktitle={Proceedings of the Genetic and Evolutionary Computation Conference Companion},
pages={1864--1871},
year={2017},
organization={ACM}
}
```
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