A GIN-Guided Multiobjective Evolutionary Algorithm for Robustness Optimization of Complex Networks

Network robustness optimization is crucial for enhancing the resilience of industrial networks and social systems against malicious attacks. Existing studies typically evaluate the robustness by simulating the sequential removal of nodes or edges and recording the residual connectivity at each step....

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Vydáno v:Algorithms Ročník 18; číslo 10; s. 666
Hlavní autoři: Li, Guangpeng, Li, Li, Cai, Guoyong
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.10.2025
Témata:
Algorithms
Analysis
Computing costs
Connectivity
Evolutionary algorithms
Genetic algorithms
graph isomorphism network
Isomorphism
Liquors
multiobjective problem
Multiple objective analysis
network robustness optimization
Optimization
Pareto optimum
Robustness
Social networks
surrogate model
Nova Scotia
ISSN:1999-4893, 1999-4893
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Shrnutí:Network robustness optimization is crucial for enhancing the resilience of industrial networks and social systems against malicious attacks. Existing studies typically evaluate the robustness by simulating the sequential removal of nodes or edges and recording the residual connectivity at each step. However, the attack simulation is computationally expensive and becomes impractical for large-scale networks. Therefore, this paper proposes a multiobjective evolutionary algorithm assisted by a graph isomorphism network (GIN)-based surrogate model to efficiently optimize network robustness. First, the robustness optimization task is formulated as a multiobjective problem that simultaneously considers network robustness against attacks and the structural modification cost. Then, a GIN-based surrogate model is constructed to approximate the robustness, replacing the expensive simulation assessments. Finally, the multiobjective evolutionary algorithm is employed to explore promising network structures guided by the surrogate model, which is continuously updated via online learning to improve both prediction accuracy and optimization performance. Experimental results in various synthetic and real-world networks demonstrate that the proposed algorithm reduces the computational cost of the robustness evaluation by about 65% while achieving comparable or even superior robustness optimization performance compared with those of baseline algorithms. These results indicate that the proposed method is practical and scalable and can be applied to enhance the robustness of industrial and social networks.
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ISSN:1999-4893
1999-4893
DOI:10.3390/a18100666