The Lion Swarm Optimization (LSO) algorithm tends to become trapped in local optima due to its reliance on inter-group collaboration during population iteration updates, particularly between lionesses and cubs in their position update strategy. Moreover, the king primarily performs limited exploration in its vicinity, thereby inevitably compromising the search for the global optimum. Its randomness and locality also produce numerous ineffective solutions. To address this issue, we refine the king's behavior by incorporating strategies from the Whale Optimization Algorithm (WOA) and introduce the rogue lion to confront the king during iterations, facilitating escape from local optima. Additionally, we employ dynamic learning strategies to enhance the position update functions of lionesses and cubs, diminishing their excessive interdependence and preventing entrapment in local optima. Furthermore, comparative evaluations on unimodal and multimodal test functions demonstrate that the improved algorithm converges rapidly. Finally, due to the limited application scenarios of the original LSO algorithm, we effectively applied the Lion Swarm-Whale Hybrid Optimization (LSWO) algorithm to optimize airport ground handling (AGH) operations. In comparison to the original algorithm, the improved version better utilizes resources and enhances operational efficiency.