Evolution algorithm with adaptive genetic operator and dynamic scoring mechanism for large-scale sparse many-objective optimization
Abstract Large-scale sparse multi-objective optimization problems are prevalent in numerous real-world scenarios, such as neural network training, sparse regression, pattern mining and critical node detection, where Pareto optimal solutions exhibit sparse characteristics. Ordinary large-scale multi-...
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Nature Portfolio
2025-03-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-91245-z |
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| author | Xia Wang Wei Zhao Jia-Ning Tang Zhong-Bin Dai Ya-Ning Feng |
| author_facet | Xia Wang Wei Zhao Jia-Ning Tang Zhong-Bin Dai Ya-Ning Feng |
| author_sort | Xia Wang |
| collection | DOAJ |
| description | Abstract Large-scale sparse multi-objective optimization problems are prevalent in numerous real-world scenarios, such as neural network training, sparse regression, pattern mining and critical node detection, where Pareto optimal solutions exhibit sparse characteristics. Ordinary large-scale multi-objective optimization algorithms implement undifferentiated update operations on all decision variables, which reduces search efficiency, so the Pareto solutions obtained by the algorithms fail to meet the sparsity requirements. SparseEA is capable of generating sparse solutions and calculating scores for each decision variable, which serves as a basis for crossover and mutation in subsequent evolutionary process. However, the scores remain unchanged in iterative process, which restricts the sparse optimization ability of the algorithm. To solve the problem, this paper proposes an evolution algorithm with the adaptive genetic operator and dynamic scoring mechanism for large-scale sparse many-objective optimization (SparseEA-AGDS). Within the evolutionary algorithm for large-scale Sparse (SparseEA) framework, the proposed adaptive genetic operator and dynamic scoring mechanism adaptively adjust the probability of cross-mutation operations based on the fluctuating non-dominated layer levels of individuals, concurrently updating the scores of decision variables to encourage superior individuals to gain additional genetic opportunities. Moreover, to augment the algorithm’s capability to handle many-objective problems, a reference point-based environmental selection strategy is incorporated. Comparative experimental results demonstrate that the SparseEA-AGDS algorithm outperforms five other algorithms in terms of convergence and diversity on the SMOP benchmark problem set with many-objective and also yields superior sparse Pareto optimal solutions. |
| format | Article |
| id | doaj-art-5e4f23b10c724cd7a62440e33a7550ee |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Nature Portfolio |
| record_format | Article |
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| spelling | doaj-art-5e4f23b10c724cd7a62440e33a7550ee2025-08-20T02:41:31ZengNature PortfolioScientific Reports2045-23222025-03-0115113410.1038/s41598-025-91245-zEvolution algorithm with adaptive genetic operator and dynamic scoring mechanism for large-scale sparse many-objective optimizationXia Wang0Wei Zhao1Jia-Ning Tang2Zhong-Bin Dai3Ya-Ning Feng4School of Electrical and Information Technology , Yunnan Minzu UniversitySchool of Electrical and Information Technology , Yunnan Minzu UniversitySchool of Electrical and Information Technology , Yunnan Minzu UniversityNanjing Branch of China Telecom Co., LtdSchool of Electrical and Information Technology , Yunnan Minzu UniversityAbstract Large-scale sparse multi-objective optimization problems are prevalent in numerous real-world scenarios, such as neural network training, sparse regression, pattern mining and critical node detection, where Pareto optimal solutions exhibit sparse characteristics. Ordinary large-scale multi-objective optimization algorithms implement undifferentiated update operations on all decision variables, which reduces search efficiency, so the Pareto solutions obtained by the algorithms fail to meet the sparsity requirements. SparseEA is capable of generating sparse solutions and calculating scores for each decision variable, which serves as a basis for crossover and mutation in subsequent evolutionary process. However, the scores remain unchanged in iterative process, which restricts the sparse optimization ability of the algorithm. To solve the problem, this paper proposes an evolution algorithm with the adaptive genetic operator and dynamic scoring mechanism for large-scale sparse many-objective optimization (SparseEA-AGDS). Within the evolutionary algorithm for large-scale Sparse (SparseEA) framework, the proposed adaptive genetic operator and dynamic scoring mechanism adaptively adjust the probability of cross-mutation operations based on the fluctuating non-dominated layer levels of individuals, concurrently updating the scores of decision variables to encourage superior individuals to gain additional genetic opportunities. Moreover, to augment the algorithm’s capability to handle many-objective problems, a reference point-based environmental selection strategy is incorporated. Comparative experimental results demonstrate that the SparseEA-AGDS algorithm outperforms five other algorithms in terms of convergence and diversity on the SMOP benchmark problem set with many-objective and also yields superior sparse Pareto optimal solutions.https://doi.org/10.1038/s41598-025-91245-zLarge-scale sparse evolutionary algorithmsAdaptive geneticsDynamic scoringSparsity of Pareto solutionsMany-objective |
| spellingShingle | Xia Wang Wei Zhao Jia-Ning Tang Zhong-Bin Dai Ya-Ning Feng Evolution algorithm with adaptive genetic operator and dynamic scoring mechanism for large-scale sparse many-objective optimization Scientific Reports Large-scale sparse evolutionary algorithms Adaptive genetics Dynamic scoring Sparsity of Pareto solutions Many-objective |
| title | Evolution algorithm with adaptive genetic operator and dynamic scoring mechanism for large-scale sparse many-objective optimization |
| title_full | Evolution algorithm with adaptive genetic operator and dynamic scoring mechanism for large-scale sparse many-objective optimization |
| title_fullStr | Evolution algorithm with adaptive genetic operator and dynamic scoring mechanism for large-scale sparse many-objective optimization |
| title_full_unstemmed | Evolution algorithm with adaptive genetic operator and dynamic scoring mechanism for large-scale sparse many-objective optimization |
| title_short | Evolution algorithm with adaptive genetic operator and dynamic scoring mechanism for large-scale sparse many-objective optimization |
| title_sort | evolution algorithm with adaptive genetic operator and dynamic scoring mechanism for large scale sparse many objective optimization |
| topic | Large-scale sparse evolutionary algorithms Adaptive genetics Dynamic scoring Sparsity of Pareto solutions Many-objective |
| url | https://doi.org/10.1038/s41598-025-91245-z |
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