MOEA/D-URAW combines uniformly random weight initialization with sparsity-based adaptive weight adjustment, yielding a decomposition-based multiobjective optimizer that adapts to different Pareto front shapes and supports flexible population sizes.
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MOEA/D with Uniformly Randomly Adaptive Weights
MOEA/D-URAW combines uniformly random weight initialization with sparsity-based adaptive weight adjustment, yielding a decomposition-based multiobjective optimizer that adapts to different Pareto front shapes and supports flexible population sizes.