Evolution algorithm with adaptive genetic operator and dynamic scoring mechanism for large-scale sparse many-objective optimization

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...

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Bibliographic Details
Published in:Scientific reports Vol. 15; no. 1; pp. 9267 - 34
Main Authors: Wang, Xia, Zhao, Wei, Tang, Jia-Ning, Dai, Zhong-Bin, Feng, Ya-Ning
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 18.03.2025
Nature Publishing Group
Nature Portfolio
Subjects:
639/705
639/705/1042
639/705/1046
639/705/117
639/705/258
Adaptive genetics
Algorithms
Dynamic scoring
Evolution
Humanities and Social Sciences
Large-scale sparse evolutionary algorithms
Many-objective
multidisciplinary
Mutation
Neural networks
Optimization
Pareto optimum
Science
Science (multidisciplinary)
Sparsity of Pareto solutions
Large-scale sparse evolutionary algorithms
Adaptive genetics
Many-objective
Sparsity of Pareto solutions
Dynamic scoring
ISSN:2045-2322, 2045-2322
Online Access:Get full text
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Summary: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.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-025-91245-z