Mitigate Cascading Failures on Networks using a Memetic Algorithm

Research concerning cascading failures in complex networks has become a hot topic. However, most of the existing studies have focused on modelling the cascading phenomenon on networks and analysing network robustness from a theoretical point of view, which considers only the damage incurred by the failure of one or several nodes. However, such a theoretical approach may not be useful in practical situation. Thus, we first design a much more practical measure to evaluate the robustness of networks against cascading failures, termed R(cf). Then, adopting R(cf) as the objective function, we propose a new memetic algorithm (MA) named MA-R(cf) to enhance network the robustness against cascading failures. Moreover, we design a new local search operator that considers the characteristics of cascading failures and operates by connecting nodes with a high probability of having similar loads. In experiments, both synthetic scale-free networks and real-world networks are used to test the efficiency and effectiveness of the MA-R(cf). We systematically investigate the effects of parameters on the performance of the MA-R(cf) and validate the performance of the newly designed local search operator. The results show that the local search operator is effective, that MA-R(cf) can enhance network robustness against cascading failures efficiently, and that it outperforms existing algorithms.