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Open-source implementations of TEVC'2010 paper, MOEA/D with Gaussian Process model

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MOEA/D-EGO

Open-source implementations for methods presented in the following papers:

  • 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]

  • 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:

  • 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.

  • An adaptive adjustment strategy for $z^*$ is used. More related discussions can be found in the Supplementary File of DirHV-EGO.

Usage

Matlab >= 2018a

Quick Start

  • The run_MOEAD_EGO.m provides the basic script to run experiments on ZDT and DTLZ.

Advanced usage

  • 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.
  • 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].

Acknowledgements

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Open-source implementations of TEVC'2010 paper, MOEA/D with Gaussian Process model

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