Weights Adaptation Optimization of Heterogeneous Epidemic Spreading Networks: A Constrained Cooperative Coevolution Strategy

TL;DR

The paper tackles dynamic constrained optimization of edge weights in heterogeneous SIS networks modeled by mean-field NIMFA, aiming to minimize infection cost over time under a budget on weight adaptation. It introduces a Constrained Cooperative Coevolution ($C^3$) framework to decompose the high-dimensional problem and couples it with Differential Evolution variants, notably NSDE, along with an $\epsilon$-constraint handling scheme. The proposed NSDE-$C^3$ method is evaluated on a Barabási–Albert network (N=20), showing superior performance in reducing infection levels while respecting the adaptation budget compared to baseline strategies. The results highlight the potential of cooperative coevolution and constrained evolutionary search for practical network-based epidemic control, with future work on online deployment and extensions to broader epidemic dynamics.

Abstract

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.

Figures (3)

• Figure 1: Schematic of the encoding mechanism.
• Figure 2: Bárabasi-Albert network
• Figure 3: Simulation results on BA network for 25 independent runs

Theorems & Definitions (5)

• Remark 4.1
• Remark 4.2
• Remark 5.1
• Remark 5.2
• Remark 5.3