← All Publications

A fast dynamic evolutionary multiobjective algorithm via manifold transfer learning

Min Jiang and Zhenzhong Wang and Liming Qiu and Shihui Guo and Xing Gao and Kay Chen Tan

IEEE TCYB 2020 · published

Links pending

Contribution

Transfers solutions through learned manifolds, making high-dimensional dynamic optimization more tractable.

Abstract

Many dynamic optimization problems are high-dimensional, with hundreds or thousands of decision variables, and the underlying landscape shifts in low-dimensional manifolds rather than in the full ambient space. This paper proposes a fast dynamic evolutionary multiobjective algorithm that learns these manifolds from historical optimal populations and transfers solutions along them when a change is detected. By exploiting manifold structure, the algorithm achieves order-of-magnitude speedups over state-of-the-art baselines on high-dimensional DMOPs.

Why This Paper Matters

If you work on expensive or large-scale dynamic optimization — supply chains, dynamic pricing, large engineering systems — this paper shows how to scale transfer-based methods to settings where they previously did not work.

BibTeX

@article{jiang2020fast,
  title={A fast dynamic evolutionary multiobjective algorithm via manifold transfer learning},
  author={Jiang, Min and Wang, Zhenzhong and Qiu, Liming and Guo, Shihui and Gao, Xing and Tan, Kay Chen},
  journal={IEEE Transactions on Cybernetics},
  volume={51},
  number={7},
  pages={3417--3428},
  year={2020},
  publisher={IEEE},
}