Dimensionality Reduction Evolutionary Framework for Solving High-Dimensional Expensive Problems

Most of improvement strategies for surrogate-assisted optimization algorithms fail to help the population quickly locate satisfactory solutions. To address this challenge, a novel framework called dimensionality reduction surrogate-assisted evolutionary (DRSAE) framework is proposed. DRSAE introduces an efficient dimensionality reduction network to create a low-dimensional search space, allowing some individuals to search in the population within the reduced space. This strategy significantly 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, correspondingly 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 substantiate the good scalability of the developed optimizer.