Metadata-Version: 2.1
Name: enoppy
Version: 0.1.0
Summary: ENOPPY: A Python Library for Engineering Optimization Problems
Home-page: https://github.com/thieu1995/enoppy
Author: Thieu
Author-email: nguyenthieu2102@gmail.com
License: GPLv3
Project-URL: Documentation, https://enoppy.readthedocs.io/
Project-URL: Source Code, https://github.com/thieu1995/enoppy
Project-URL: Bug Tracker, https://github.com/thieu1995/enoppy/issues
Project-URL: Change Log, https://github.com/thieu1995/enoppy/blob/master/ChangeLog.md
Project-URL: Forum, https://t.me/+fRVCJGuGJg1mNDg1
Description: 
        <p align="center"><img src=".github/img/logo.png" alt="ENOPPY" title="ENOPPY"/></p>
        
        ---
        
        
        
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        ENOPPY (ENgineering Optimization Problems in PYthon) is the largest python library for real-world engineering 
        optimization problems. Contains all engineering problems from CEC competition functions from 2005, 2008, 2010, 2013, 
        2014, 2015, 2017, 2019, 2020, 2021, 2022. 
        
        * **Free software:** GNU General Public License (GPL) V3 license
        * **Total problems**: > 50 problems
        * **Documentation:** https://enoppy.readthedocs.io/en/latest/
        * **Python versions:** 3.6.x, 3.7.x, 3.8.x, 3.9.x, 3.10.x
        * **Dependencies:** numpy, scipy, pandas, matplotlib
        
        
        
        
        # Installation
        
        ### Install with pip
        
        Install the [current PyPI release](https://pypi.python.org/pypi/enoppy):
        ```sh 
        $ pip install enoppy==0.1.0
        ```
        
        Or install the development version from GitHub:
        
        ```bash
        pip install git+https://github.com/thieu1995/enoppy
        ```
        
        ### Install from source
        
        In case you want to install directly from the source code, use:
        ```sh 
        $ git clone https://github.com/thieu1995/enoppy.git
        $ cd enoppy
        $ python setup.py install
        ```
        
        
        ## Lib's structure
        
        ```code 
        
        docs
        examples
        enoppy
            paper_based
                pdo_2022.py
                rwco_2020.py
            problem_based
                chemical.py
                mechanism.py
            utils
                operator.py
                validator.py
                visualize.py
            __init__.py
            engineer.py
        README.md
        setup.py
        ```
        
        
        # Usage
        
        After installation, you can import ENOPPY as any other Python module:
        
        ```sh
        $ python
        >>> import enoppy
        >>> enoppy.__version__
        ```
        
        Let's go through some examples.
        
        
        ### Examples
        
        How to get the function and use it
        
        #### 1st way
        
        ```python
        from enoppy.cec_based.cec2014 import F12014
        
        func = F12014(ndim=30)
        func.evaluate(func.create_solution())
        
        ## or
        
        from enoppy.cec_based import F102014
        
        func = F102014(ndim=50)
        func.evaluate(func.create_solution())
        ```
        
        
        #### 2nd way
        
        ```python
        
        import enoppy
        
        funcs = enoppy.get_functions_by_classname("F12014")
        func = funcs[0](ndim=10)
        func.evaluate(func.create_solution())
        
        ## or
        
        all_funcs_2014 = enoppy.get_functions_based_classname("2014")
        print(all_funcs_2014)
        
        ```
        
        For more usage examples please look at [examples](/examples) folder.
        
        
        
        ### Get helps (questions, problems)
        
        * Official source code repo: https://github.com/thieu1995/enoppy
        * Official document: https://enoppy.readthedocs.io/
        * Download releases: https://pypi.org/project/enoppy/
        * Issue tracker: https://github.com/thieu1995/enoppy/issues
        * Notable changes log: https://github.com/thieu1995/enoppy/blob/master/ChangeLog.md
        * Examples with different meapy version: https://github.com/thieu1995/enoppy/blob/master/examples.md
        
        * This project also related to our another projects which are "meta-heuristics" and "neural-network", check it here
            * https://github.com/thieu1995/mealpy
            * https://github.com/thieu1995/metaheuristics
            * https://github.com/thieu1995/permetrics
            * https://github.com/aiir-team
        
        
        **Want to have an instant assistant? Join our telegram community at [link](https://t.me/+fRVCJGuGJg1mNDg1)**
        We share lots of information, questions, and answers there. You will get more support and knowledge there.
        
        
        ### Cite Us
        
        If you are using enoppy in your project, we would appreciate citations:
        
        ```code 
        @software{thieu_nguyen_2020_3711682,
          author       = {Nguyen Van Thieu},
          title        = {ENOPPY: A Python Library for Engineering Optimization Problems},
          year         = 2020,
          publisher    = {Zenodo},
          doi          = {10.5281/zenodo.3620960},
          url          = {https://doi.org/10.5281/zenodo.3620960.}
        }
        ```
        
        
        ### References 
        
        ```code
        1. http://benchmarkfcns.xyz/fcns
        2. https://en.wikipedia.org/wiki/Test_functions_for_optimization
        3. https://www.cs.unm.edu/~neal.holts/dga/benchmarkFunction/
        4. http://www.sfu.ca/~ssurjano/optimization.html
        5. A Literature Survey of Benchmark Functions For Global Optimization Problems (2013)
        6. Problem Definitions and Evaluation Criteria for the CEC 2014 Special Session and Competition on Single Objective Real-Parameter Numerical Optimization 
        ```
        
Keywords: optimization,metaheuristics,MHA,mathematical optimization,nature-inspired algorithms,evolutionary computation,soft computing,population-based algorithms,Stochastic optimization,Global optimization,Convergence analysis,Search space exploration,Local search,Computational intelligence,Black-box optimization,Robust optimization,Hybrid algorithms,Benchmark functions,Metaheuristic design,Performance analysis,Exploration versus exploitation,Self-adaptation,Constrained optimization,Intelligent optimization,Adaptive search,Simulations,Algorithm selection
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Information Technology
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: System :: Benchmark
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Mathematics
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Software Development :: Build Tools
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Utilities
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
