Metadata-Version: 2.1
Name: pysga
Version: 1.2.4
Summary: Search Group Algorithm metaheuristic optimization method python adaptation
Home-page: https://github.com/Ginklings/pysga
Author: André Ginklings
Author-email: andre.ginklings@gmail.com
License: LICENSE.txt
Description: # pysga
        A python adaptation to matlab Search Group Algorithm code
        
        The Search Group Algorithm (SGA) is a metaheuristic optimization method for nonlinear, nonconvex, nonsmooth, multimodal, bounded optimization problems. You may also find a tutorial in a pdf file, which is a step by step explanation about how to use the SGA code.
        The sections and equations cited in this file refer to the paper that presented the SGA:
        M.S. Gonçalves, R.H. Lopez, L.F.F. Miguel, Search group algorithm: A new metaheuristic method for the optimization of truss structures, Computers & Structures, 153:165-184, 2015. DOI: 0.1016/j.compstruc.2015.03.003
        This paper may also be download at Research Gate:
        
        https://www.researchgate.net/publication/274253521_Search_group_algorithm_A_new_metaheuristic_method_for_the_optimization_of_truss_structures
        
        or from science direct at:
        
        http://www.sciencedirect.com/science/article/pii/S0045794915000851
        
        The m-files original codes is provide from https://www.mathworks.com/matlabcentral/fileexchange/50598-search-group-algorithm-matlab-code
        
        ## Installation:
        
        ### Requeriments:
        Actually is working in python 3.x. The following modules are necessary:
        * numpy (all)
        * kivy (for app only)
        
        ### Install
        Use pip to install. For only the function without GUI App:
        ```bash
        pip install pysga
        ```
        
        This will install numpy if necessary.
        
        For GUI App:
        ```bash
        pip install pysga[full]
        ```
        
        This will install the kivy module and dependencies. For any error, consult de kivy documentation.
        
        ## App example:
        ```python
        from pysga.sgaApp import SearchGroupAlgorithmApp
        from kivy.config import Config
        Config.set('graphics', 'width', '500')
        Config.set('graphics', 'height', '600')
        app = SearchGroupAlgorithmApp()
        app.run()
        ```
        
        Put a fobj_function.py file in current directory and define your objective function as fobj function.
        
        When run the app, choose the from file option and run the optimizer.
        
        ![Alt text](OptimizationParams.png?raw=true "Pameters of SGA optimizer")
        
        ![Alt text](FunctionParams.png?raw=true "Function configuration")
        
        ## Call SGA in python code:
        
        See the example in github website.
        
        
Keywords: metaheuristic optimization algorithm
Platform: Windows
Platform: Unix
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Description-Content-Type: text/x-rst
Provides-Extra: full
