Vehicle cruise control systems have become essential components of modern automobiles, offering significant benefits in driving comfort, fuel efficiency, and road safety. An effective control strategy is crucial for ensuring reliable performance. In this paper, a real PIDD \(^{2}\) (proportional–integral–derivative plus second-order derivative) controller is proposed to address the challenges associated with achieving efficient and robust cruise control. To optimally tune the real PIDD \(^{2}\) controller parameters, we develop a novel metaheuristic approach that enhances the white shark optimization (WSO) algorithm using Lévy flight and the Nelder–Mead method. While Lévy flight improves global exploration, the Nelder–Mead method strengthens local exploitation, resulting in the proposed L-WSONM algorithm. To validate its robustness and effectiveness, L-WSONM is extensively tested on complex optimization problems from the CEC2020 benchmark suite and compared with several high-performance algorithms, including gray wolf, pufferfish, golden jackal, reptile search, white shark, tunicate swarm, prairie dog, and seagull optimizers. The results show that L-WSONM consistently delivers superior solution quality and faster convergence rates, with the exception of functions F2 and F9. Finally, L-WSONM is applied to fine-tune the parameters of the real PIDD \(^{2}\) controller for vehicle cruise control. Simulation results demonstrate that the L-WSONM-based real PIDD \(^{2}\) controller achieves zero overshoot, a rise time of 0.463 s, and a settling time of 0.692 s, outperforming the original WSO and other recent approaches from the literature.
