An enhanced discrete dung beetle optimizer with multi-strategy for influence maximization

Abstract Influence maximization (IM) aims to select a set of influential nodes in social networks to maximize information diffusion. However, existing methods struggle to balance solution accuracy and computational efficiency, restricting their practical deployment as network size increases. This paper proposes a Discrete Dung Beetle Optimizer for the IM problem. We discretize the Dung Beetle Optimizer and introduce an SD-based initialization to enhance population diversity and convergence speed