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MOBO/D

Multiobjective Bayesian Optimization based on Decomposition

Multiobjective Bayesian Optimization based on Decomposition

Decomposition is a fundamental strategy for solving multiobjective optimization problems (MOPs), as it enables the simplification of MOPs into more manageable sub-problems [1]. This strategy not only makes the problem-solving process more efficient, but also provides a flexible way to manage the diversity of solutions. This page includes the representative MultiObjective Bayesian Optimization (MOBO) papers that utilize decomposition techniques.

1. Random Scalarization

Applie the standard single-objective acquisition function after random scalarization.

  • 2006, TEVC. Joshua Knowles. "ParEGO: a Hybrid Algorithm with on-line Landscape Approximation for Expensive Multiobjective Optimization Problems". [PDF]

Randomly select a scalarized acquisition function.

  • 2020, UAI. Biswajit Paria, Kirthevasan Kandasamy, Barnabás Póczos. "A Flexible Framework for Multi-Objective Bayesian Optimization using Random Scalarizations". [PDF]
  • 2020, ICML. Richard Zhang, Daniel Golovin. "Random Hypervolume Scalarizations for Provable Mmulti-objective Black Box Optimization". [PDF]

2. Beyond Random Scalarization

2.1 Decomposition & Cooperation

Following the MOEA/D framework, decompose a MOP in question into a number of sub-problems via a set of reference vectors and solve these sub-problems in a collaborative manner. It is based on the assumption that the optimal solutions of two subproblems should be similar when their reference vectors are close.

  • 2010, TEVC. MOEA/D-EGO. Qingfu Zhang, Wudong Liu, Edward Tsang, and Botond Virginas. "Expensive Multiobjective Optimization by MOEA/D with Gaussian Process Model". [PDF] [code]
  • 2017, TEVC. Namura, Nobuo, Koji Shimoyama, and Shigeru Obayashi. "Expected improvement of penalty-based boundary intersection for expensive multiobjective optimization." [PDF]
  • 2024, TEVC. DirHV-EGO. Liang Zhao and Qingfu Zhang. "Hypervolume-Guided Decomposition for Parallel Expensive Multiobjective Optimization". [PDF] [Supplementary] [code]

2.2 Pareto Set Learning

  • 2022, NeurIPS. PSL-MOBO. Xi Lin, Zhiyuan Yang, Xiaoyuan Zhang, and Qingfu Zhang. "Pareto Set Learning for Expensive Multi-Objective Optimization". [PDF] [Supplementary] [code]

3. Relationship between Indicator-based and Decomposition-based Algorithms

  • 2024, TEVC. Liang Zhao, Xiaobin Huang, Chao Qian, and Qingfu Zhang. "Many-to-Few Decomposition: Linking R2-based and Decomposition-based Multiobjective Efficient Global Optimization Algorithms". [PDF] [Supplementary] [code]

Popular repositories Loading

  1. DirHV-EGO DirHV-EGO Public

    Open-source implementation of TEVC'2024 paper "Hypervolume-Guided Decomposition for Parallel Expensive Multiobjective Optimization"

    MATLAB 6 1

  2. MOEAD-EGO MOEAD-EGO Public

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

    MATLAB 4

  3. .github .github Public

  4. R2D-EGO R2D-EGO Public

    Open-source implementation of TEVC'2024 paper "Many-to-Few Decomposition: Linking R2-based and Decomposition-based Multiobjective Efficient Global Optimization Algorithms"

    MATLAB

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Showing 4 of 4 repositories
  • DirHV-EGO Public

    Open-source implementation of TEVC'2024 paper "Hypervolume-Guided Decomposition for Parallel Expensive Multiobjective Optimization"

    mobo-d/DirHV-EGO’s past year of commit activity
    MATLAB 6 MIT 1 0 0 Updated Oct 31, 2024
  • .github Public
    mobo-d/.github’s past year of commit activity
    0 0 0 0 Updated Oct 21, 2024
  • R2D-EGO Public

    Open-source implementation of TEVC'2024 paper "Many-to-Few Decomposition: Linking R2-based and Decomposition-based Multiobjective Efficient Global Optimization Algorithms"

    mobo-d/R2D-EGO’s past year of commit activity
    MATLAB 0 MIT 0 0 0 Updated Sep 20, 2024
  • MOEAD-EGO Public

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

    mobo-d/MOEAD-EGO’s past year of commit activity
    MATLAB 4 MIT 0 0 0 Updated Jun 20, 2024

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