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A sparse large-scale multi-objective evolutionary algorithm based on sparsity detection
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DOI：10.1016/j.swevo.2024.101820.png)
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Abstract
En 中文
Sparse large-scale multi-objective optimization problems (LSMOPs), in which most decision variables of the Pareto-optimal solutions are zero, have become increasingly prevalent in real-world applications. An effective approach for addressing the sparse LSMOPs is the two-layer encoding scheme. However, a significant challenge when adapting the two-layer encoding scheme remains in accurately identifying the sparse distribution of Pareto-optimal solutions. Therefore, this paper proposes a sparse large-scale multi-objective evolutionary algorithm based on sparsity detection. The proposed algorithm uses the two-layer encoding scheme with a specialized focus on finding the positions of sparse non-zero variables by optimizing the binary vector. In the algorithm, an initialization strategy based on sparsity detection is proposed. This strategy employs a sparsity detection method to identify non-zero variables at the beginning of the algorithm. Moreover, a two- stage search strategy is designed to enhance the ability to search the sparse Pareto-optimal solutions. In this strategy, two types of knowledge are adaptively selected to guide the genetic operators of the binary vector. Experimental results from the benchmark suite and real-world applications demonstrate that the proposed algorithm outperforms the existing state-of-the-art algorithms in solving sparse LSMOPs.
Keywords:
Evolutionary algorithm
Large-scale multi-objective optimization
Sparse optimization
Initialization
Two-stage evolutionary
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