Package: niapy
Nature-inspired algorithms are a very popular tool for solving optimization problems. Numerous variants of nature-inspired algorithms have been developed (paper 1,paper 2) since the beginning of their era. To prove their versatility, those were tested in various domains on various applications, especially when they are hybridized, modified or adapted. However, implementation of nature-inspired algorithms is sometimes a difficult, complex and tedious task. In order to break this wall, NiaPy is intended for simple and quick use, without spending time for implementing algorithms from scratch.Free software:MIT licenseDocumentation:https://niapy.readthedocs.io/en/stable/Python versions:3.9.x, 3.10.x, 3.11.x, 3.12.xDependencies:click hereMissionOur mission is to build a collection of nature-inspired algorithms and create a simple interface for managing the optimization process. NiaPy offers:numerous optimization problem implementations,use of various nature-inspired algorithms without struggle and effort with a simple interface,easy comparison between nature-inspired algorithms, andexport of results in various formats such as Pandas DataFrame, JSON or even Excel.InstallationInstall NiaPy with pip:pipinstallniapyTo install NiaPy with conda, use:condainstall-cniaorgniapyTo install NiaPy on Fedora, use:dnfinstallpython3-niapyTo install NiaPy on Arch Linux, please use anAUR helper:yay-Syyupython-niapyTo install NiaPy on Alpine Linux, please enable Community repository and use:apkaddpy3-niapyTo install NiaPy on NixOS, please use:nix-env-iAnixos.python310Packages.niapyTo install NiaPy on Void Linux, use:xbps-install-Spython3-niapyInstall from sourceIn case you want to install directly from the source code, use:pipinstallgit+https://github.com/NiaOrg/NiaPy.gitAlgorithmsClick herefor the list of implemented algorithms.ProblemsClick herefor the list of implemented test problems.UsageAfter installation, you can import NiaPy as any other Python module:$python
>>>importniapy
>>>niapy.__version__Let's go through a basic and advanced example.Basic ExampleLet’s say, we want to try out PSO against the Pintér problem function. Firstly, we have to create new file, with name, for examplebasic_example.py. Then we have to import chosen algorithm from NiaPy, so we can use it. Afterwards we initialize ParticleSwarmAlgorithm class instance and run the algorithm. Given bellow is the complete source code of basic example.fromniapy.algorithms.basicimportParticleSwarmAlgorithmfromniapy.taskimportTask# we will run 10 repetitions of Weighted, velocity clamped PSO on the Pinter problemforiinrange(10):task=Task(problem='pinter',dimension=10,max_evals=10000)algorithm=ParticleSwarmAlgorithm(population_size=100,w=0.9,c1=0.5,c2=0.3,min_velocity=-1,max_velocity=1)best_x,best_fit=algorithm.run(task)print(best_fit)Given example can be run withpython basic_example.pycommand and should give you similar output as following:0.0087735348908636460.036616190934621755186.751168125925460.024186452828927896263.569746983734845.4207069243659160.69467536110913677.7561002047805685.8396733144259070.06732518679742806Advanced ExampleIn this example we will show you how to implement a custom problem class and use it with any of
implemented algorithms. First let's create new file named advanced_example.py. As in the previous examples
we wil import algorithm we want to use from niapy module.For our custom optimization function, we have to create new class. Let's name itMyProblem. In the initialization
method ofMyProblemclass we have to set thedimension,lowerandupperbounds of the problem. Afterwards we have to
override the abstract method _evaluate which takes a parameterx, the solution to be evaluated, and returns the function value.
Now we should have something similar as is shown in code snippet bellow.importnumpyasnpfromniapy.taskimportTaskfromniapy.problemsimportProblemfromniapy.algorithms.basicimportParticleSwarmAlgorithm# our custom problem classclassMyProblem(Problem):def__init__(self,dimension,lower=-10,upper=10,*args,**kwargs):super().__init__(dimension,lower,upper,*args,**kwargs)def_evaluate(self,x):returnnp.sum(x**2)Now, all we have to do is to initialize our algorithm as in previous examples and pass an instance of our MyProblem class as the problem argument.my_problem=MyProblem(dimension=20)foriinrange(10):task=Task(problem=my_problem,max_iters=100)algo=ParticleSwarmAlgorithm(population_size=100,w=0.9,c1=0.5,c2=0.3,min_velocity=-1,max_velocity=1)# running algorithm returns best found minimumbest_x,best_fit=algo.run(task)# printing best minimumprint(best_fit)Now we can run our advanced example with following command:python advanced_example.py. The results should be similar to those bellow.0.0024556140507614760.0005576529723921640.00297913256798654130.00094435952745253360.0010126588244920690.00068372368928160720.00267897257746854950.0050177469930046010.00116544734023221960.0019074442166293853For more usage examples please look atexamplesfolder.More advanced examples can also be found in theNiaPy-examples repository.Cite usAre you using NiaPy in your project or research? Please cite us!Plain formatVrbančič, G., Brezočnik, L., Mlakar, U., Fister, D., & Fister Jr., I. (2018).
      NiaPy: Python microframework for building nature-inspired algorithms.
      Journal of Open Source Software, 3(23), 613\. <https://doi.org/10.21105/joss.00613>Bibtex format@article{NiaPyJOSS2018,
        author  = {Vrban{\v{c}}i{\v{c}}, Grega and Brezo{\v{c}}nik, Lucija
                  and Mlakar, Uro{\v{s}} and Fister, Du{\v{s}}an and {Fister Jr.}, Iztok},
        title   = {{NiaPy: Python microframework for building nature-inspired algorithms}},
        journal = {{Journal of Open Source Software}},
        year    = {2018},
        volume  = {3},
        issue   = {23},
        issn    = {2475-9066},
        doi     = {10.21105/joss.00613},
        url     = {https://doi.org/10.21105/joss.00613}
    }RIS formatTY  - JOUR
    T1  - NiaPy: Python microframework for building nature-inspired algorithms
    AU  - Vrbančič, Grega
    AU  - Brezočnik, Lucija
    AU  - Mlakar, Uroš
    AU  - Fister, Dušan
    AU  - Fister Jr., Iztok
    PY  - 2018
    JF  - Journal of Open Source Software
    VL  - 3
    IS  - 23
    DO  - 10.21105/joss.00613
    UR  - http://joss.theoj.org/papers/10.21105/joss.00613Contributors ✨Thanks goes to these wonderful people (emoji key):Grega Vrbančič💻📖🐛💡🚧📦📆👀firefly-cpp💻📖🐛💡👀💬⚠️📦Lucija Brezočnik💻📖🐛💡mlaky88💻📖💡rhododendrom💻📖💡🐛👀Klemen💻📖💡🐛👀Jan Popič💻📖💡Luka Pečnik💻📖💡🐛Jan Banko💻📖💡RokPot💻📖💡mihaelmika💻📖💡Jace Browning💻Musa Adamu Wakili💬Florian Schaefer🤔Jan-Hendrik Menke💬brett18618💬Timotej Zaťko🐛sisco0💻zStupan💻🐛📖💡⚠️Tomáš Hrnčiar💻Ikko Ashimine💻andrazperson💻Oromion📦This project follows theall-contributorsspecification. Contributions of any kind are welcome!ContributingWe encourage you to contribute to NiaPy! Please check out theContributing to NiaPy guidefor guidelines about how to proceed.Everyone interacting in NiaPy's codebases, issue trackers, chat rooms and mailing lists is expected to follow the NiaPycode of conduct.LicenceThis package is distributed under the MIT License. This license can be found online athttp://www.opensource.org/licenses/MIT.DisclaimerThis framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!