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A hypergraph-oriented MOABC for multi-objective influence maximization and important-element evaluation in interactive systems
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DOI：10.1016/j.eswa.2026.133788.png)
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Abstract
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• Task hypergraphs capture high-order dependencies in interaction systems. • A bee-colony solver searches variable-size seed sets across budgets. • Specialized set operators guide search on unordered hypergraph seed sets. • Experiments use eight human-system cases and one maritime interface case. • The method improves trade-offs and task coverage over strong baselines.
Keywords:
Artificial bee colony algorithm
Interactive system
Influence maximization
Hypergraph model
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