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Multiple Classifiers-Assisted Evolutionary Algorithm Based on Decomposition for High-Dimensional Multi-Objective Problems

preprint
posted on 25.07.2021, 16:20 by Takumi Sonoda, Masaya NakataMasaya Nakata
Surrogate-assisted multi-objective evolutionary algorithms have advanced the field of computationally expensive optimization, but their progress is often restricted to low-dimensional problems. This manuscript presents a multiple classifiers-assisted evolutionary algorithm based on decomposition, which is adapted for high-dimensional expensive problems in terms of the following two insights. Compared to approximation-based surrogates, the accuracy of classification-based surrogates is robust for few high-dimensional training samples. Further, multiple local classifiers can hedge the risk of over-fitting issues. Accordingly, the proposed algorithm builds multiple classifiers with support vector machines on a decomposition-based multi-objective algorithm, wherein each local classifier is trained for a corresponding scalarization function. Experimental results statistically confirm that the proposed algorithm is competitive to the state-of-the-art algorithms and computationally efficient as well.

Funding

JSPS KAKENHI Grant Number 20H04254

History

Email Address of Submitting Author

nakata-masaya-tb@ynu.ac.jp

ORCID of Submitting Author

0000-0003-3428-7890

Submitting Author's Institution

Yokohama National University

Submitting Author's Country

Japan