A Constrained Cooperative Coevolution Strategy for Weights Adaptation Optimization of Heterogeneous Epidemic Spreading Networks
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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 following is that the dimension of the weights adaptation optimization problem is increasing exponentially with the network size and most existing evolutionary algorithms cannot achieve satisfied 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 tailored for this problem. Meanwhile, the ϵ constraint-handling technique is employed to achieve the tradeoff between constraint and objective function. To validate the effectiveness of the proposed method, some numerical simulations are conducted on a Bárabasi-Albert network.
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