Weights Adaptation Optimization of Heterogeneous Epidemic Spreading Networks: A Constrained Cooperative Coevolution Strategy
This work addresses a domain-specific problem in epidemic modeling, focusing on improving optimization performance for large-scale networks, but it appears incremental as it builds on existing evolutionary algorithms.
The paper tackles the dynamic constrained optimization problem of weights adaptation for heterogeneous epidemic spreading networks by employing a novel constrained cooperative coevolution (C^3) strategy to separate large-scale problems into subcomponents, achieving a trade-off between constraints and objective functions.
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.