Open-source implementations for methods presented in the following papers:
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Q. Zhang, W. Liu, E. Tsang, and B. Virginas. Expensive multiobjective optimization by MOEA/D with Gaussian process model. IEEE Transactions on Evolutionary Computation, 2009. [PDF]
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L. Zhao and Q. Zhang. Exact Formulas for the Computation of Expected Tchebycheff Improvement. Proceedings of the IEEE Congress on Evolutionary Computation, 2023.
The Java Code of MOEA/D-EGO (written by Wudong Liu) is avaliable at this website. Our implementation differs slightly from the vanilla MOEA/D-EGO in two aspects:
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The FuzzyCM is removed. FuzzyCM is an approximation method for GP modeling. It could reduce the computational time when training the GP models. However, MOEA/D-EGO without FuzzyCM could perform better in terms of solution quality. Interested readers can find more related discussions in Section VII-E of MOEA/D-EGO.
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An adaptive adjustment strategy for
$z^*$ is used. More related discussions can be found in the Supplementary File of DirHV-EGO.
Matlab >= 2018a
- The
run_MOEAD_EGO.mprovides the basic script to run experiments on ZDT and DTLZ.
- Download PlatEMO (version 4.6) and read PlatEMO's User Manual to familiarize yourself with how to use this platform.
- Copy the folders within "Algorithms" into the directory at "PlatEMO/Algorithms/". Next, add all of the subfolders contained within the "PlatEMO" directory to the MATLAB search path.
- In the MATLAB command window, type
platemo()to run PlatEMO using the GUI. - Select the label "expensive" and choose the algorithm "MOEA-D-EGO".
- Default setting of
batch size: 5.
- Default setting of
- Select a problem and set appropriate parameters.
- e.g., ZDT1, N=200, M=2, D=8, maxFE=200.
- e.g., Inverted DTLZ2, N=210, M=3, D=6, maxFE=300.
If you have any questions or feedback, please feel free to contact [email protected] and [email protected].
- This implementation is based on PlatEMO.
- For GP modeling, we employe the DACE toolbox.