Journal of Computer Applications ›› 2024, Vol. 44 ›› Issue (11): 3530-3539.DOI: 10.11772/j.issn.1001-9081.2023111659
• Advanced computing • Previous Articles Next Articles
Jie HUANG1, Ruizi WU2, Junli LI1,3()
Received:
2023-11-29
Revised:
2024-01-22
Accepted:
2024-02-05
Online:
2024-02-29
Published:
2024-11-10
Contact:
Junli LI
About author:
HUANG Jie, born in 1998, M. S. candidate. Her research interests include complex networks, evolutionary algorithms, deep learning.通讯作者:
李均利
作者简介:
黄杰(1998—),女,四川成都人,硕士研究生,主要研究方向:复杂网络、进化算法、深度学习CLC Number:
Jie HUANG, Ruizi WU, Junli LI. Efficient adaptive robustness optimization algorithm for complex networks[J]. Journal of Computer Applications, 2024, 44(11): 3530-3539.
黄杰, 武瑞梓, 李均利. 高效的自适应复杂网络鲁棒性优化算法[J]. 《计算机应用》唯一官方网站, 2024, 44(11): 3530-3539.
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URL: https://www.joca.cn/EN/10.11772/j.issn.1001-9081.2023111659
网络类型 | 预测误差/10-3 |
---|---|
ER | 3.7 |
SF | 4.6 |
SW | 4.2 |
Tab. 1 Prediction errors of surrogate model for different network types
网络类型 | 预测误差/10-3 |
---|---|
ER | 3.7 |
SF | 4.6 |
SW | 4.2 |
网络 | 算法 | 鲁棒性值 | 运行 时间/103s | 单位优化 时间/102s |
---|---|---|---|---|
ER (DIR) | SU-ANet | 0.335 8 | 6.3 | 8.0 |
GE-SU-EANet | 0.334 1(+) | 6.4 | 8.4 | |
MA | 0.339 1(-) | 40.0 | 49.0 | |
GA | 0.322 9(+) | 6.3 | 9.7 | |
SF (DIR) | SU-ANet | 0.272 5 | 6.5 | 4.8 |
GE-SU-EANet | 0.250 8(+) | 6.2 | 5.5 | |
MA | 0.276 4(≈) | 85.0 | 61.0 | |
GA | 0.228 7(+) | 5.9 | 6.5 | |
SW (DIR) | SU-ANet | 0.372 1 | 6.2 | 10.0 |
GE-SU-EANet | 0.370 8(+) | 6.4 | 11.0 | |
MA | 0.376 4(-) | 41.0 | 67.0 | |
GA | 0.359 3(+) | 5.2 | 12.0 | |
ER (UDR) | SU-ANet | 0.328 7 | 6.1 | 8.3 |
GE-SU-EANet | 0.328 1(≈) | 6.0 | 8.4 | |
MA | 0.332 3(-) | 28.0 | 37.0 | |
GA | 0.307 5(+) | 5.0 | 9.8 | |
SF (UDR) | SU-ANet | 0.243 8 | 5.1 | 5.3 |
GE-SU-EANet | 0.242 7(≈) | 5.9 | 6.2 | |
MA | 0.247 8(-) | 27.0 | 27.0 | |
GA | 0.228 6(+) | 5.1 | 6.3 | |
SW (UDR) | SU-ANet | 0.365 2 | 5.2 | 7.4 |
GE-SU-EANet | 0.365 6(≈) | 6.0 | 8.5 | |
MA | 0.368 0(-) | 29.0 | 40.0 | |
GA | 0.344 1(+) | 3.9 | 8.1 |
Tab. 2 Performance comparison of algorithms in synthetic network experiments
网络 | 算法 | 鲁棒性值 | 运行 时间/103s | 单位优化 时间/102s |
---|---|---|---|---|
ER (DIR) | SU-ANet | 0.335 8 | 6.3 | 8.0 |
GE-SU-EANet | 0.334 1(+) | 6.4 | 8.4 | |
MA | 0.339 1(-) | 40.0 | 49.0 | |
GA | 0.322 9(+) | 6.3 | 9.7 | |
SF (DIR) | SU-ANet | 0.272 5 | 6.5 | 4.8 |
GE-SU-EANet | 0.250 8(+) | 6.2 | 5.5 | |
MA | 0.276 4(≈) | 85.0 | 61.0 | |
GA | 0.228 7(+) | 5.9 | 6.5 | |
SW (DIR) | SU-ANet | 0.372 1 | 6.2 | 10.0 |
GE-SU-EANet | 0.370 8(+) | 6.4 | 11.0 | |
MA | 0.376 4(-) | 41.0 | 67.0 | |
GA | 0.359 3(+) | 5.2 | 12.0 | |
ER (UDR) | SU-ANet | 0.328 7 | 6.1 | 8.3 |
GE-SU-EANet | 0.328 1(≈) | 6.0 | 8.4 | |
MA | 0.332 3(-) | 28.0 | 37.0 | |
GA | 0.307 5(+) | 5.0 | 9.8 | |
SF (UDR) | SU-ANet | 0.243 8 | 5.1 | 5.3 |
GE-SU-EANet | 0.242 7(≈) | 5.9 | 6.2 | |
MA | 0.247 8(-) | 27.0 | 27.0 | |
GA | 0.228 6(+) | 5.1 | 6.3 | |
SW (UDR) | SU-ANet | 0.365 2 | 5.2 | 7.4 |
GE-SU-EANet | 0.365 6(≈) | 6.0 | 8.5 | |
MA | 0.368 0(-) | 29.0 | 40.0 | |
GA | 0.344 1(+) | 3.9 | 8.1 |
网络 | 算法 | 鲁棒性值 | 运行 时间/104 | 单位优化 时间/103 |
---|---|---|---|---|
SF(500) | SU-ANet | 0.217 5 | 2.2 | 2.6 |
GE-SU-EANet | 0.205 2(+) | 2.4 | 3.3 | |
MA | 0.222 2(≈) | 3.7 | 4.1 | |
GA | 0.195 3(+) | 1.8 | 2.8 | |
SF(800) | SU-ANet | 0.204 4 | 4.6 | 6.1 |
GE-SU-EANet | 0.190 6(+) | 4.9 | 8.0 | |
MA | 0.209 2(-) | 8.1 | 10.0 | |
GA | 0.178 3(+) | 4.1 | 8.3 | |
SF(1 000) | SU-ANet | 0.194 2 | 7.0 | 12.0 |
GE-SU-EANet | 0.179 2(+) | 7.3 | 16.0 | |
MA | 0.203 8(-) | 11.0 | 16.0 | |
GA | 0.171 9(+) | 6.5 | 18.0 |
Tab. 3 Performance comparison of algorithms in algorithm scalability experiments
网络 | 算法 | 鲁棒性值 | 运行 时间/104 | 单位优化 时间/103 |
---|---|---|---|---|
SF(500) | SU-ANet | 0.217 5 | 2.2 | 2.6 |
GE-SU-EANet | 0.205 2(+) | 2.4 | 3.3 | |
MA | 0.222 2(≈) | 3.7 | 4.1 | |
GA | 0.195 3(+) | 1.8 | 2.8 | |
SF(800) | SU-ANet | 0.204 4 | 4.6 | 6.1 |
GE-SU-EANet | 0.190 6(+) | 4.9 | 8.0 | |
MA | 0.209 2(-) | 8.1 | 10.0 | |
GA | 0.178 3(+) | 4.1 | 8.3 | |
SF(1 000) | SU-ANet | 0.194 2 | 7.0 | 12.0 |
GE-SU-EANet | 0.179 2(+) | 7.3 | 16.0 | |
MA | 0.203 8(-) | 11.0 | 16.0 | |
GA | 0.171 9(+) | 6.5 | 18.0 |
网络 | 算法 | 鲁棒性值 | 运行 时间/103 | 单位优化 时间/102 |
---|---|---|---|---|
USAIR97 | SU-ANet | 0.215 9 | 8.5 | 11.0 |
GE-SU-EANet | 0.208 0(+) | 9.6 | 14.0 | |
MA | 0.243 5(-) | 26.0 | 25.0 | |
GA | 0.189 4(+) | 3.5 | 6.8 | |
POWER-494 | SU-ANet | 0.318 2 | 9.5 | 15.0 |
GE-SU-EANet | 0.314 1(+) | 8.4 | 15.0 | |
MA | 0.327 2(-) | 44.0 | 63.0 | |
GA | 0.292 0(+) | 6.5 | 18.0 |
Tab. 4 Performance comparison of algorithms in real-world network experiments
网络 | 算法 | 鲁棒性值 | 运行 时间/103 | 单位优化 时间/102 |
---|---|---|---|---|
USAIR97 | SU-ANet | 0.215 9 | 8.5 | 11.0 |
GE-SU-EANet | 0.208 0(+) | 9.6 | 14.0 | |
MA | 0.243 5(-) | 26.0 | 25.0 | |
GA | 0.189 4(+) | 3.5 | 6.8 | |
POWER-494 | SU-ANet | 0.318 2 | 9.5 | 15.0 |
GE-SU-EANet | 0.314 1(+) | 8.4 | 15.0 | |
MA | 0.327 2(-) | 44.0 | 63.0 | |
GA | 0.292 0(+) | 6.5 | 18.0 |
1 | GIRVAN M, NEWMAN M E J. Community structure in social and biological networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2002, 99(12): 7821-7826. |
2 | HOFMAN J M, SHARMA A, WATTS D J. Prediction and explanation in social systems[J]. Science, 2017, 355(6324): 486-488. |
3 | EBEL H, MIELSCH L I, BORNHOLDT S. Scale-free topology of e-mail networks[J]. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 2002, 66(3): No.035103. |
4 | FALOUTSOS M, FALOUTSOS P, FALOUTSOS C. On power-law relationships of the internet topology[M]// NEWMAN M, BARABÁSI A L, WATTS D J. The Structure and Dynamics of Networks. Princeton: Princeton University Press, 2006: 195-206. |
5 | WANDELT S, SHI X, SUN X. Estimation and improvement of transportation network robustness by exploiting communities[J]. Reliability Engineering and System Safety, 2021, 206: No.107307. |
6 | COHEN R, EREZ K, BEN-AVRAHAM D, et al. Breakdown of the internet under intentional attack[J]. Physical Review Letters, 2001, 86(16): No.3682. |
7 | SCHNEIDER C M, MOREIRA A A, ANDRADE J S, Jr., et al. Mitigation of malicious attacks on networks[J]. Proceedings of the National Academy of Sciences of the United States of America, 2011, 108(10): 3838-3841. |
8 | ZENG A, LIU W. Enhancing network robustness against malicious attacks[J]. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 2012, 85(6): No.066130. |
9 | BULDYREV S, PARSHANI R, PAUL G, et al. Catastrophic cascade of failures in interdependent networks[J]. Nature, 2010, 464(7291): 1025-1028. |
10 | MERRIS R. Laplacian matrices of graphs: a survey[J]. Linear Algebra and its Applications, 1994, 197/198: 143-176. |
11 | WANG S, LIU J. Constructing robust cooperative networks using a multi-objective evolutionary algorithm[J]. Scientific Reports, 2017, 7: No.41600. |
12 | WANG S, LIU J. Designing comprehensively robust networks against intentional attacks and cascading failures[J]. Information Sciences, 2019, 478: 125-140. |
13 | WANG S, LIU J, JIN Y. Surrogate-assisted robust optimization of large-scale networks based on graph embedding[J]. IEEE Transactions on Evolutionary Computation, 2020, 24(4): 735-749. |
14 | ZHOU M, LIU J. A memetic algorithm for enhancing the robustness of scale-free networks against malicious attacks[J]. Physica A: Statistical Mechanics and its Applications, 2014, 410: 131-143. |
15 | TANG X, LIU J, ZHOU M. Enhancing network robustness against targeted and random attacks using a memetic algorithm[J]. EPL (EuroPhysics Letters), 2015, 111(3): No.38005. |
16 | ZHOU M, LIU J. A two-phase multiobjective evolutionary algorithm for enhancing the robustness of scale-free networks against multiple malicious attacks[J]. IEEE Transactions on Cybernetics, 2017, 47(2): 539-552. |
17 | TANG X, LIU J, HAO X. Mitigate cascading failures on networks using a memetic algorithm[J]. Scientific Reports, 2016, 6: No.38713. |
18 | WANG S, LIU J. Community robustness and its enhancement in interdependent networks[J]. Applied Soft Computing, 2019, 77: 665-677. |
19 | WANG S, LIU J, JIN Y. Robust structural balance in signed networks using a multiobjective evolutionary algorithm[J]. IEEE Computational Intelligence Magazine, 2020, 15(2): 24-35. |
20 | CHEN J, LIU J. A memetic algorithm for optimizing inter-links to enhance the robustness of interdependent networks against malicious attacks[C]// Proceedings of the 2021 IEEE Congress on Evolutionary Computation. Piscataway: IEEE, 2021: 327-334. |
21 | WANG S, LIU J, JIN Y. A computationally efficient evolutionary algorithm for multiobjective network robustness optimization[J]. IEEE Transactions on Evolutionary Computation, 2021, 25(3): 419-432. |
22 | LOU Y, HE Y, WANG L, et al. Predicting network controllability robustness: a convolutional neural network approach[J]. IEEE Transactions on Cybernetics, 2022, 52(5): 4052-4063. |
23 | LOU Y, HE Y, WANG L, et al. Knowledge-based prediction of network controllability robustness[J]. IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(10): 5739-5750. |
24 | LOU Y, WU R, LI J, et al. A learning convolutional neural network approach for network robustness prediction[J]. IEEE Transactions on Cybernetics, 2023, 53(7): 4531-4544. |
25 | LOU Y, WU R, LI J, et al. A convolutional neural network approach to predicting network connectedness robustness[J]. IEEE Transactions on Network Science and Engineering, 2021, 8(4): 3209-3219. |
26 | KENNEDY J, EBERHART R. Particle swarm optimization[C]// Proceedings of the 1995 International Conference on Neural Networks — Volume 4. Piscataway: IEEE, 1995: 1942-1948. |
27 | ZHANG J, SANDERSON A C. JADE: adaptive differential evolution with optional external archive[J]. IEEE Transactions on Evolutionary Computation, 2009, 13(5): 945-958. |
28 | QIN A K, SUGANTHAN P N. Self-adaptive differential evolution algorithm for numerical optimization[C]// Proceedings of the 2005 IEEE Congress on Evolutionary Computation — Volume 2. Piscataway: IEEE, 2005: 1785-1791. |
29 | LIU J, LAMPINEN J. A fuzzy adaptive differential evolution algorithm[J]. Soft Computing, 2005, 9(6): 448-462. |
30 | BREST J, GREINER S, BOSKOVIC B, et al. Self-adapting control parameters in differential evolution: a comparative study on numerical benchmark problems[J]. IEEE Transactions on Evolutionary Computation, 2006, 10(6): 646-657. |
31 | TEO J. Exploring dynamic self-adaptive populations in differential evolution[J]. Soft Computing, 2006, 10(8): 673-686. |
32 | MOSCATO P. On evolution, search, optimization, genetic algorithms and martial arts: towards memetic algorithms[EB/OL]. [2023-09-12]. . |
33 | LIU J, ABBASS H A, TAN K C. Evolutionary Computation and Complex Networks[M]. Cham: Springer, 2019. |
34 | WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]// Proceedings of the 2018 European Conference on Computer Vision, LNCS 11211. Cham: Springer, 2018: 3-19. |
35 | ERDÖS P, RÉNYI A. On the evolution of random graphs[M]// NEWMAN M, BARABÁSI A L, WATTS D J. The Structure and Dynamics of Networks. Princeton: Princeton University Press, 2006: 38-82. |
36 | BARABÁSI A L, ALBERT R. Emergence of scaling in random networks[J]. Science, 1999, 286(5439): 509-512. |
37 | WATTS D J, STROGATZ S H. Collective dynamics of ‘small-world’ networks[J]. Nature, 1998, 393(6684): 440-442. |
38 | KRUSKAL W H, WALLIS W A. Use of ranks in one-criterion variance analysis[J]. Journal of the American Statistical Association, 1952, 47(260): 583-621. |
39 | ROSSI R A, AHMED N K. The network data repository with interactive graph analytics and visualization[C]// Proceedings of the 39th AAAI Conference on Artificial Intelligence. Palo Alto, CA: AAAI Press, 2015: 4292-4293. |
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