Search Group Algorithm metaheuristic optimization method python adaptation
Project 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:
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:
pip install pysga
This will install numpy if necessary.
For GUI App:
pip install pysga[full]
This will install the kivy module and dependencies. For any error,
consult de kivy documentation.
App example:
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 github website.
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