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An expensive multi-objective evolutionary algorithm based on grid and relation learning
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AI
DOI：10.1016/j.asoc.2025.114135.png)
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Abstract
En 中文
• Proposes a relationship-based surrogate model trained on pairwise solution comparisons. • Employs grid-based ranking to select superior solutions for constructing training pairs. • Proposes GRE-MOEA to solve multi-objective problems with limited function evaluations. • Validates the algorithm on problems with up to 50 variables and 10 objectives. • Demonstrates superior performance on both benchmark and real-world problems.
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