In a network, influence maximization addresses identifying an optimal set of nodes to initiate influence propagation, thereby maximizing the influence spread. Current approaches for influence maximization encounter limitations in accuracy and efficiency. Furthermore, most existing methods are aimed at the IC (Independent Cascade) diffusion model, and few solutions concern dynamic networks. In this study, we focus on dynamic networks consisting of UAV (Unmanned Aerial Vehicle) clusters that perform coverage tasks and introduce IMUNE, an evolutionary algorithm for influence maximization in UAV networks. We first generate dynamic networks that simulate UAV coverage tasks and give the representation of dynamic networks. Novel fitness functions in the evolutionary algorithm are designed to estimate the influence ability of a set of seed nodes in a dynamic process. On this basis, an integrated fitness function is proposed to fit both the IC and SI (Susceptible–Infected) models. IMUNE can find seed nodes for maximizing influence spread in dynamic UAV networks with different diffusion models through the improvements in fitness functions and search strategies. Experimental results on UAV network datasets show the effectiveness and efficiency of the IMUNE algorithm in solving influence maximization problems.

中文翻译：

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IMUNE：一种用于无人机网络影响力最大化的新型进化算法


在网络中，影响力最大化解决了确定一组最佳节点以启动影响力传播的问题，从而最大限度地提高了影响力的传播。当前的影响力最大化方法在准确性和效率方面遇到了限制。此外，大多数现有方法都针对 IC（独立级联）扩散模型，很少有解决方案涉及动态网络。在本研究中，我们专注于由执行覆盖任务的 UAV（无人机）集群组成的动态网络，并引入了 IMUNE，这是一种用于无人机网络影响力最大化的进化算法。我们首先生成模拟无人机覆盖任务的动态网络，并给出动态网络的表示。进化算法中新颖的适应度函数旨在估计一组种子节点在动态过程中的影响能力。在此基础上，提出了一个集成适应度函数来拟合 IC 和 SI （Susceptible-Infected） 模型。IMUNE 可以通过改进适应度函数和搜索策略，找到种子节点，以最大化具有不同扩散模型的动态无人机网络中的影响力扩散。在无人机网络数据集上的实验结果表明，IMUNE 算法在解决影响最大化问题方面的有效性和效率。