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The CDO algorithm mimics the dynamic behavior of cloud particles influenced by atmospheric forces, striking a refined balance between exploration and exploitation. It features an adaptive weight adjustment mechanism that alters the cloud\u2019s real-time drift behavior, allowing for efficient navigation through the search space. Using a cloud-based drift strategy, CDO harnesses probabilistic movements to maneuver through the optimization landscape more effectively. The algorithm has undergone rigorous testing against various established unimodal and multimodal benchmark functions, where it showcases outstanding performance characterized by faster convergence rates, high robustness, and exceptional solution accuracy compared to top contemporary optimization techniques. Additionally, CDO applies to numerous real-world engineering optimization tasks, such as designing cantilever beams, three-bar trusses, tension\/compression springs, and pressure vessels. 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