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A dual-population knowledge transfer framework with adaptive operator selection for constrained multi-objective optimization
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DOI：10.1016/j.asoc.2026.115591.png)
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Abstract
En 中文
• A dual-population framework is proposed for constrained multi-objective optimization. • Adaptive operator selection dynamically switches between GA and DE. • Dynamic knowledge transfer adjusts migration based on offspring effectiveness. • Experimental results demonstrate improved convergence, diversity, and feasibility.
Keywords:
constrained multi-objective optimization
dual-population framework
adaptive operator selection
knowledge transfer
evolutionary algorithms
Journal
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6.6
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1.4W
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4.8W