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Global optimization is challenging, particularly in high-dimensional and multimodal search spaces characterized by complex landscapes and numerous local optima. This paper proposes Wave, a novel physical-Based metaheuristic optimizer, which combines wave-inspired oscillatory factors, Lévy-based random flights, and adaptive exploration and exploitation strategies to tackle global optimization problems. Inspired by the cyclical nature of wave phenomena, our approach exploits time-varying sinusoidal amplitudes that gradually reduce while maintaining oscillatory behavior, thus enhancing both population diversity and local search. However, in Wave, the random flights derived from heavy-tailed step distributions provide additional large jumps that aid in escaping local minima. Wave has been evaluated over CEC2022 benchmark functions; the results demonstrate that Wave exhibits a strong convergence performance and comparable results with several state-of-the-art metaheuristic optimizers. For example, Wave outperformed all compared optimizers in F1, F6, F11 and opined the first rank when solving the cantilver beam engineering design problem. The obtained results highlights the effectiveness of wave-driven exploration and targeted exploitation strategies, paving the way for broader applications in engineering design and other complex optimization problems.