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A sparse large-scale multi-objective evolutionary optimization based on bi-level interactive grouping
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AI
DOI：10.1016/j.swevo.2025.102209.png)
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Abstract
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Sparse large-scale multi-objective optimization problems present a dual challenge: a vast number of decision variables and highly sparse Pareto-optimal sets, where traditional evolutionary algorithms often fail. To tackle these issues, we propose a Bi-level Interactive Grouping Evolutionary Algorithm (BLIGEA). The algorithm’s novelty lies in two main contributions. First, it introduces a bi-level interactive grouping strategy that applies distinct optimization mechanisms to the binary and real vectors of the solutions, fostering their synergistic co-evolution. Moreover, a knowledge-guided strategy is designed to effectively learn and leverage sparsity information from the population during the search process. Extensive experimental results on benchmarks and real-world applications demonstrate that BLIGEA significantly surpasses state-of-the-art methods.
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