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

Autor: Haibo Yu, Yiyun Gong, Li Kang, Chaoli Sun, Jianchao Zeng
Jazyk: angličtina
Rok vydání: 2023
Předmět:
Zdroj: Complex & Intelligent Systems, Vol 10, Iss 1, Pp 171-191 (2023)
Druh dokumentu: article
ISSN: 2199-4536
2198-6053
DOI: 10.1007/s40747-023-01168-3
Popis: 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.
Databáze: Directory of Open Access Journals
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