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Representation embedded learning via autoencoder for large-scale multi-objective optimization
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DOI：10.1016/j.asoc.2025.114065.png)
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Abstract
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• A Representation Embedded Learning via Autoencoders algorithm (RELA) is proposed for solving large-scale multi-objective optimization. • A representation embedded learning strategy is proposed to learn latent representations. • An encoding reconstruction strategy is proposed to generate higher-quality solution sets. • Two offspring generation strategies are proposed to balance convergence and diversity. • RELA achieves excellent results on two benchmark suites and a real-world problem.
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