Moth-flame optimization for critical node detection

The critical node detection (CND) is after removing these critical nodes of a given number, there is the least network connectivity on a predefined measure for the residual network. The CND arouses great attention for its extensive practical applications, however, the CND is still computationally challenging until now. In this paper, we present an improved moth-flame optimization for CND, in which opposition-based learning and fast population evolution strategy are implemented to generate solutions with diversity and accelerate the convergence, and hybrid Gaussian evolution method helps the proposed algorithm to get rid of local optima traps and enhance its exploration ability. In our experiments, the performance of proposed algorithm is evaluated on the synthetic networks and real-world networks, and we also make comparisons with other algorithms. The experimental results illustrate that the proposed algorithm outperforms other current algorithms.