Structure optimization based on memetic algorithm for adjusting epidemic threshold on complex networks

Many social spreading phenomena can be modeled as epidemic spreading models over networks, and the studies of these phenomena are important to avoid epidemic outbreaks. Epidemic threshold of the network, which fundamentally depends on the network structure itself, is a critical measure to judge whether the epidemic dies out or results in an epidemic breakout. In this study, epidemic threshold is regarded as the objective function to control the spreading process. In addition, an efficient structure optimization strategy based on memetic algorithm is proposed to adjust the spreading threshold without changing the degree of each node. Lowering the threshold can promote the spreading process whereas heightening the threshold can prevent the spreading process. In the proposed algorithm, genetic algorithm is adopted as the global search strategy and a modified simulated annealing algorithm combined with the properties of networks is proposed as the local search strategy. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm has superior performances for both the threshold minimization and maximization problems. (C) 2016 Elsevier B.V. All rights reserved.