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Fuzzy clustering-based large-scale multimodal multi-objective differential evolution algorithm
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AI
DOI：10.1016/j.swevo.2025.101856.png)
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Abstract
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Traditional multi-modal multi-objective problems often have multiple local optima in the decision space, and each local optimum is of great importance. However, real-world multi-modal problems are often largescale problems, and there are few algorithms specifically designed for large-scale multi-modal multi-objective problems. Some proposed algorithms have been tested only on specific problems and are not applicable to solve other specific problems. Based on this problem, this paper proposes a large-scale multi-modal multi- objective differential evolution algorithm called LMMODE, based on fuzzy clustering. The Fuzzy C-means(FCM) algorithm, suitable for high-dimensional data, is employed to divide the search space into multiple subspaces. The multi-stage optimization approach is then utilized to balance the algorithm's performance in the objective space and decision space through different strategies, thereby solving large-scale multi-modal multi-objective problems. Experimental results demonstrate that, compared to state-of-the-art multi-modal and large-scale algorithms, LMMODE is competitive in solving large-scale multi-modal problems.
Keywords:
Multimodal multi-objective optimization
Differential evolution
Large-scale optimization
Fuzzy C-means
Evolutionary computation
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