pysga 1.2.4

Search Group Algorithm metaheuristic optimization method python adaptation

# 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.



## Call SGA in python code:

See the example in github website.