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This source code implement the winner of the Large-Scale Global Optimization Competition organized in IEEE Congress of Evolutionary Computation 2018, http://www.tflsgo.org/special_sessions/cec2018.html

The implementation is done in Python 3, using numpy.

This source code is freely available under the General Public License (GPLv3). However, if you use it in a research paper, you should refer to the original work:

"Molina, D., LaTorre, A. Herrera, F. SHADE with Iterative Local Search for Large-Scale Global Optimization. Proceeding of the 2018, IEEE Congress on Evolutionary Computation, Rio de Janeiro, Brasil, 8-13 July, 2018, pp 1252-1259"

It was presented in the WCCI 2018, in particular in the IEEE Congress on Evolutionary Computation. The slides are available.

It is recommended to use

source install.sh

That command will create a virtual environment (virtualenv) in the directory venv with all required dependencies.

The source code is prepared for doing the experiments using the Large-Scale Global Optimization CEC'2013 benchmark.

Parameters:

python shadeils -f -s [-r ] ...

• function is the number of function (between 1-15).
• run is the number of run for evaluations.
• seed is a seed value (integer value between 1 and 5).

There are other optional parameters, you can run

python shadeils.py -h```

to get the descriptions of the different optional parameters.