A Reinforcement Learning-Driven Multi-Agent Cooperative Grey Wolf Algorithm for Influence Maximization

Influence maximization (IM) in social networks aims to identify the optimal set of seed nodes that maximizes influence spread under a given diffusion model. The standard Grey Wolf Optimizer (GWO) suffers from two fundamental limitations when applied to this problem: an inflexible exploration–exploitation transition controlled by a linearly decreasing parameter; and a rigid three-level leadership hierarchy that suppresses individual diversity and promotes premature convergence. In this paper, we propose a Multi-Role Cooperative Grey Wolf Optimizer (Multiple-roles GWO) that addresses both limitations through two complementary mechanisms. First, a Q-learning-based adaptive phase transition mechanism monitors population diversity, fitness improvement rate, and iteration progress in real time, enabling the algorithm to dynamically shift between exploration and exploitation. Second, inspired by the principle of division of labor, the exploitation phase is restructured into a four-role cooperative framework comprising leaders, explorers, followers, and losers, each executing a distinct search strategy to improve local search coverage and maintain population diversity. Experiments on six real-world social networks under the Independent Cascade model show that Multiple-roles GWO achieves competitive or superior influence spread compared with state-of-the-art heuristic baselines, with comparable computational efficiency.