pysga 1.2.7

Search Group Algorithm metaheuristic optimization method python adaptation

pysga

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A python adaptation for 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:

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Actually is working in python 3.x. The following modules are necessary:

* numpy (all) * kivy (for app only)

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:

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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:

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See the github website.