The article introduces CLMEA, a new algorithm for high-dimensional expensive multi-objective optimization problems. It combines classifier-assisted rank-based learning with local model-based evolutionary strategies to explore uncertain but informative spaces efficiently. The algorithm aims to improve convergence and diversity in solving complex optimization problems. Experimental results demonstrate the superior performance of CLMEA compared to existing algorithms on benchmark problems and real-world applications.
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by Guodong Chen... at arxiv.org 03-19-2024
https://arxiv.org/pdf/2304.09444.pdfDeeper Inquiries