Optimized Weights for Heterogeneous Epidemic Spreading Networks: A Constrained Cooperative Coevolution Strategy

The wide spreading of COVID-19 all over the world raises numerous focus on the epidemic containment problem. Different from the traditional epidemic control strategies that focus on quarantine and vaccination, we seek to control the epidemic from a network science perspective, i.e., by adjusting the weights of the epidemic spreading networks. Moreover, considering the limitations on the available resources, the dynamic constrained optimization problem of weights’ adaptation for heterogeneous epidemic spreading networks is investigated. Due to the powerful ability of searching for global optimum, evolutionary algorithms (EAs) are used as optimizers. One major difficulty is that the dimension of the problem is increasing exponentially with the network size and most existing EAs cannot achieve satisfiable performance on large-scale optimization problems. To address this issue, a novel constrained cooperative coevolution (
$C^{3}$
) strategy, which can separate the original large-scale problem into different subcomponents, is used to achieve the tradeoff between the constraint and objective function.