Spatial-temporal Knowledge Transfer for Dynamic Constrained Multiobjective Optimization

Min Jiang; Zihao Zhang; Gary G. Yen

Contribution

Introduces a spatial-temporal knowledge transfer framework that simultaneously exploits spatial correlations among decision variables and temporal patterns across environmental changes for dynamic constrained multiobjective optimization.

Abstract

Dynamic constrained multiobjective optimization problems present dual challenges: time-varying objectives and changing constraints. This paper proposes a spatial-temporal knowledge transfer framework that exploits both spatial correlations among decision variables and temporal patterns across environmental changes, enabling more effective adaptation than purely temporal approaches.

Why This Paper Matters

Extends transfer learning in dynamic optimization to the constrained setting, and introduces the novel idea of leveraging spatial structure in addition to temporal patterns.

When You May Find This Relevant

You may find this paper relevant if your work involves dynamic constrained optimization, spatial-temporal modeling in evolutionary computation, or knowledge transfer under changing constraints.

When solving dynamic constrained multiobjective optimization problems
When designing spatial-temporal knowledge transfer mechanisms

BibTeX

@article{jiang2024spatial,
  title={Spatial-temporal Knowledge Transfer for Dynamic Constrained Multiobjective Optimization},
  author={Jiang, Min and Zhang, Zihao and Yen, Gary G.},
  journal={IEEE Transactions on Evolutionary Computation},
  year={2024},
  note={BibTeX entry pending verification}
}

Min Jiang, Zihao Zhang, Yew-Soon Ong, Gary G. Yen · IEEE Transactions on Evolutionary Computation, 2024

Proposes a transfer learning framework that leverages historical optimization knowledge to accelerate convergence in dynamic multiobjective environments.

Knee Point-Based Imbalanced Transfer Learning for Dynamic Multiobjective Optimization

Zihao Zhang, Min Jiang, Gary G. Yen · IEEE Transactions on Cybernetics, 2024

Addresses the imbalanced distribution problem in transfer learning for dynamic multiobjective optimization by leveraging knee point information to guide knowledge transfer.

Metadata

{
  "title": "Spatial-temporal Knowledge Transfer for Dynamic Constrained Multiobjective Optimization",
  "authors": "Min Jiang, Zihao Zhang, Gary G. Yen",
  "year": 2024,
  "venue": "IEEE Transactions on Evolutionary Computation",
  "type": "journal",
  "status": "published",
  "doi": "pending",
  "topics": [
    "Dynamic Multiobjective Optimization",
    "Learning-driven Optimization"
  ],
  "keywords": [
    "dynamic constrained multiobjective optimization",
    "spatial-temporal knowledge transfer",
    "evolutionary computation"
  ]
}