Dual-drive collaboration surrogate-assisted evolutionary algorithm by coupling feature reduction and reconstruction

Abstract

Abstract Surrogate-assisted evolutionary algorithm (SAEA) prevails in the optimization of computationally expensive problems. However, existing SAEAs confront low efficiency in the resolution of high-dimensional problems characterized by multiple local optima and multivariate coupling. To this end, this paper offers a dual-drive collaboration surrogate-assisted evolutionary algorithm (DDCSAEA) by coupling feature reduction and reconstruction, which coordinates two unsupervised feature learning techniques, i.e., principal component analysis and autoencoder, in tandem. DDCSAEA creates a low-dimensional solution space by downscaling the target high-dimensional space via principal component analysis and collects promising candidates in the reduced space by collaborating a surrogate-assisted evolutionary sampling with differential mutation. An autoencoder is used to perform the feature reconstruction on the collected candidates for infill-sampling in the target high-dimensional space to sequentially refine the neighborhood landscapes of the optimal solution. Experimental results reveal that DDCSAEA has stronger convergence performance and optimization efficiency against eight state-of-the-art SAEAs on high-dimensional benchmark problems within 200 dimensions.

Keywords

• Expensive problems
• Surrogate model
• Principal component analysis
• Autoencoder
• Evolutionary algorithms
• Feature reduction and reconstruction