[JOSS] Comment on existing libraries/frameworks

[ljvmiranda921]

[JOSS] Comment on existing libraries/frameworks

[firefly-cpp]

Dear @ljvmiranda921,

Thank you very much for your detailed comments! The following projects will be mentioned in our paper:

DEAP

@Article{deap,
author = " F'elix-Antoine Fortin and Fran\c{c}ois-Michel {De Rainville} and Marc-Andr'e Gardner and Marc Parizeau and Christian Gagn'e ",
title = { {DEAP}: Evolutionary Algorithms Made Easy },
pages = { 2171--2175 },
volume = { 13 },
year = { 2012 },
journal = { Journal of Machine Learning Research }
}

EvoloPy

@inproceedings{evolopy,
title={EvoloPy: An Open-source Nature-inspired Optimization Framework in Python.},
author={Faris, Hossam and Aljarah, Ibrahim and Mirjalili, Seyedali and Castillo, Pedro A and Merelo, Juan J},
booktitle={Proceedings of the 8th International Joint Conference on Computational Intelligence},
pages={171--177},
year={2016}
}

PySwarms

@Article{pyswarms,
author = {Lester James V. Miranda},
title = "{P}y{S}warms, a research-toolkit for {P}article {S}warm {O}ptimization in {P}ython",
journal = {Journal of Open Source Software},
year = {2018},
volume = {3},
issue = {21},
doi = {10.21105/joss.00433},
url = {https://doi.org/10.21105/joss.00433}
}

inspyred

@Article{inspyred,
title={inspyred: Bio-inspired Algorithms in Python},
author={Garrett, Aaron},
journal={URL: https://pypi. python. org/pypi/inspyred},
year={2014}
}

Pygmo

@inproceedings{pygmo,
title={Pygmo and pykep: Open source tools for massively parallel optimization in astrodynamics (the case of interplanetary trajectory optimization)},
author={Izzo, Dario},
booktitle={Proceedings of the Fifth International Conference on Astrodynamics Tools and Techniques, ICATT},
year={2012}
}

Do you have any additional suggestions?

[ljvmiranda921]

Hi, this looks good already. Also, please add a sentence or two in the paper describing how Niapy relates to these frameworks. Doesn't need to be a full-blown RRL 👍

[firefly-cpp]

Hi @ljvmiranda921 ,

The following text is going to be added in paper.

"There are also some existing similar Python implementations/frameworks of nature-inspired algorithms, as for example DEAP [@DEAP], EvoloPy [@evolopy], PySwarms [@pyswarms], inspyred [@inSPYred] and Pygmo [@pygmo]. However, NiaPy tends to be very minimalistic and easy for use, especially for practitioners and students. "

[firefly-cpp]

This is also related to issue: #122

[ljvmiranda921]

This looks good to me, I appreciate citing PySwarms but the library has more of a niche scope (only swarm optimization and its variants) compared to others (and your work) that offers optimizers outside of swarm opt. You can drop it if you deem it unnecessary.

#121 and #122 can be closed once the branches are merged. 👍

[firefly-cpp]

Dear @ljvmiranda921,

I really believe that PySwarms framework has a place in related work section. It is a good example of
a well-documented and extensible framework, where other SI algorithms can also be easily developed within the framework.

Thanks again for your quick responses.