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Probabilistic variable selection-based evolutionary algorithm for large-scale sparse multiobjective optimization
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DOI：10.1007/s40747-026-02346-9.png)
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Abstract
En 中文
Large-scale sparse multi-objective optimization problems (LSMOPs) are characterized by Pareto-optimal solutions in which only a small subset of decision variables are truly critical and take nonzero values. Efficiently identifying these variables and generating sparse Pareto-optimal solutions in high-dimensional spaces remains a major challenge. To address this issue, this paper proposes a probabilistic variable selection method, which precisely assigns each decision variable a probability of being selected as a critical variable based on the evolutionary information of the population. In generating each offspring solution, unlike existing LSMOEAs that can only handle a limited subset of decision variables or select a single variable as a critical variable at a time, the proposed algorithm can simultaneously determine whether each decision variable is selected as a critical variable, directly setting a large number of non-critical variables to zero. This enhances exploration capabilities in high-dimensional decision spaces while maintaining stable performance across different scales of LSMOPs. Extensive experiments on benchmark and real-world LSMOPs demonstrate that the proposed algorithm achieves faster identification of sparse structures and more thorough optimization of critical variables, consistently outperforming most state-of-the-art LSMOEAs across different problem scales.
Keywords:
Large-scale multi-objective optimization
Sparse Pareto optimal solutions
Evolutionary algorithm
Real-world sparse applications
Journal
C
IF:
4.6
Papers:
242
Citations:
0