Dimensionality Reduction Evolutionary Framework for Solving High-Dimensional Expensive Problems

Most of improvement strategies for surrogate-assisted optimiza-tion algorithms fail to help the population quickly locate satis-factory solutions. To address this challenge, a novel framework called dimensionality reduction surrogate-assisted evolutionary (DRSAE) framework is proposed. DRSAE introduces an effi-cient dimensionality reduction network to create a low-dimensional search space, allowing some individuals to search in the population within the reduced space. This strategy signifi-cantly lowers the complexity of the search space and makes it easier to locate promising regions. Meanwhile, a hierarchical search is conducted in the high-dimensional space. Lower-level particles indiscriminately learn from higher-level peers, corre-spondingly the highest-level particles undergo self-mutation. A comprehensive comparison between DRSAE and mainstream HEPs algorithms was conducted using seven widely used benchmark functions. Comparison experiments on problems with dimensionality increasing from 50 to 200 further substanti-ate the good scalability of the developed optimizer.

@article{SONGWei2024,
  title     = {Dimensionality Reduction Evolutionary Framework for Solving High-Dimensional Expensive Problems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {9},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {SONGWei and ZOUFucai},
  doi       = {10.14569/IJACSA.2024.0150962},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150962}
}