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A novel information entropy-based decomposition-integration multi-objective evolutionary algorithm for balancing exploration and exploitation
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DOI：10.1016/j.swevo.2026.102489.png)
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Abstract
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• Proposes IE-MOEA/DI, a novel entropy-based decomposition-integration MOEA for multi-objective optimization. • Develops an information entropy metric to quantify population dispersion in the decision space. • Designs a Riesz s-Energy based initial population to eliminate random initialization bias. • Builds an integrated scalarization framework to balance exploration and exploitation. • Proposes a search angle-based similar elite crossover strategy to improve MOEAs' convergence speed and accuracy.
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