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An efficient dominance decomposition-based deep graph evolutionary algorithm for the expensive multi-objective optimization
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DOI：10.1016/j.eswa.2026.131379.png)
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摘要
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• Proposes a dominance decomposition-based deep graph evolutionary algorithm. • Decomposes complex dominance prediction into sub-objective superiority learning. • Introduces graph neural networks into expensive multi-objective optimization. • Improves efficiency via multi-task surrogate modeling and cluster filtering. • Demonstrates strong performance on benchmark and real-world optimization tasks.
Keyword:
dominance decomposition
graph neural networks
multi-objective optimization
surrogate modeling
evolutionary algorithm
期刊
IF:
7.5
论文数:
3.0W
被引数:
10.2W
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