  In this paper, the dynamic constrained optimization problem of weights
adaptation for heterogeneous epidemic spreading networks is investigated. Due
to the powerful ability of searching global optimum, evolutionary algorithms
are employed as the optimizers. One major difficulty is that the dimension of
the problem is increasing exponentially with the network size and most existing
evolutionary algorithms 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 employed to achieve the trade-off
between the constraint and objective function.
