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A problem transformation method based on elite-inspired evolutionary algorithm for large-scale multi-objective optimization
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DOI：10.1016/j.asoc.2025.114085.png)
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Abstract
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• Opposition-Based Learning (OBL) enhances the diversity of reference solutions. • Co-evolution between problem transformation and EIEA enhances exploration and exploitation. • Dynamic reference solution updates improve convergence and solution quality in transformed spaces. • The improved EIEA balances diversity and convergence, enhancing optimization performance.
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