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Improved state transition algorithm with variable-capacity archive for constrained multiobjective optimization
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AI
DOI：10.1016/j.eswa.2025.130563.png)
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Abstract
En 中文
• A novel algorithm is proposed for CMOPs with narrow/disconnected feasible regions. • A variable-capacity archive dynamically balances objectives/constraints in evolution. • Improve three key state transition algorithm operators for fine-grained search. • 37 benchmarks and 10 real-world problems show it outperforms/rivals SOTA algorithms.
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7.5
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3.0W
Citations:
11.7W
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