Abstract
Networks can represent many real-world complex systems. Systems like internet, power grids and fuel distribution networks need to be robust and capable of surviving from failures or intentional attacks. In recent years, the measurements node-robustness and link-robustness have attracted many researchers, and some researchers use different methods to enhance one of them or both of them. In this paper, we put forward a new method which is to use a multi-objective evolutionary algorithm to enhance both these two kinds of robustness of networks against attacks. We define two objective functions which represent node-robustness and link-robustness respectively. Experiments show that our algorithm can find a good balance between improving node-robustness and link-robustness.
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References
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. U.S.A. 99(12), 7821–7826 (2002)
Crucitti, P., Latora, V., Marchiori, M., et al.: Error and attack tolerance of complex networks. Nature 406(6794), 542 (2000)
Schneider, C.M., Moreira, A.A., Andrade Jr., J.S., Havlin, S., Herrmann, H.J.: Mitigation of malicious attacks on networks. Proc. Natl. Acad. Sci. U.S.A. 108(10), 3838–3841 (2011)
Buesser, P., Daolio, F., Tomassini, M.: Optimizing the robustness of scale-free networks with simulated annealing. In: Dobnikar, A., Lotrič, U., Šter, B. (eds.) ICANNGA 2011. LNCS, vol. 6594, pp. 167–176. Springer, Heidelberg (2011)
Louzada, V.H.P., Daolio, F., Herrmann, H.J., Tomassini, M.: Smart rewiring for network robustness. J. Complex Netw. 1, 150–159 (2013)
Zhou, M., Liu, J.: A memetic algorithm for enhancing the robustness of scale-free networks against malicious attacks. Phys. A Stat. Mech. Appl. 410(12), 131–143 (2014)
Zeng, A., Liu, W.: Enhancing network robustness for malicious attacks. Physics 85(6), 3112–3113 (2012)
Deb, K., Pratap, A., Agarwal, S., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Albert, R., Barabási, A.L.: Statistical mechanics of complex networks. Rev. Mod. Phys. 74, 47–97 (2002)
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Li, Z., Wang, S., Ma, W. (2016). Multi-objective Evolutionary Algorithm for Enhancing the Robustness of Networks. In: Gong, M., Pan, L., Song, T., Zhang, G. (eds) Bio-inspired Computing – Theories and Applications. BIC-TA 2016. Communications in Computer and Information Science, vol 682. Springer, Singapore. https://doi.org/10.1007/978-981-10-3614-9_39
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DOI: https://doi.org/10.1007/978-981-10-3614-9_39
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