.png){kind=icon}
Return
Generative model-based evolutionary algorithm for solving large-scale sparse multi-objective optimization problems
.png){kind=icon}
PRE
AI
DOI：10.1016/j.asoc.2026.116100.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Large-scale sparse multi-objective optimization problems (LSMOPs) are a class of optimization problems widely encountered in engineering practice and scientific research, where the optimal solutions typically contain only a small number of nonzero decision variables. Existing large-scale sparse multi-objective evolutionary algo rithms (LSMOEAs) mainly rely on heuristic sparsity operators and local recombination mechanisms to search for high-quality solutions, which are insufficient to characterize the complex nonlinear dependencies and po tential coupling patterns among decision variables. To address this issue, this paper proposes a generative model-based evolutionary algorithm for large-scale sparse multi-objective optimization, which no longer re lies solely on traditional crossover and mutation operators but instead performs distribution-level modeling of the current non-dominated solutions and directly samples and decodes candidate solutions in a low-dimensional latent space. Through this learning-driven direct sampling mechanism, the search process is elevated from local perturbations to distribution-level generation, enabling the algorithm to adaptively capture the distributional characteristics of Pareto optimal solutions. To evaluate the effectiveness of the proposed algorithm in solving LSMOPs with varying levels of complexity, comprehensive experiments are conducted on multiple benchmark suites and real-world problems. The results demonstrate that, in most cases, the proposed algorithm outperforms existing state-of-the-art LSMOEAs in terms of both convergence and diversity of the obtained solutions.
Keywords:
Multi-objective optimization
Sparse optimization
Evolutionary computation
Large-scale optimization
Dimensionality reduction
Journal
IF:
6.6
Papers:
1.4W
Citations:
4.8W
Organization
Cited Papers
No cited papers available