## 1 Introduction  
The rapid advancement of engineering and technological fields has significantly transformed the landscape of design and problem-solving. As industries push the boundaries of innovation, they face increasingly intricate and multifaceted design problems that demand not only creativity but also rigorous analytical methods [1]. Traditional optimization techniques often fall short in addressing these complex challenges due to the inherent nature of modern design problems. These problems are frequently characterized by high dimensionality, where the number of variables or parameters involved is vast, leading to a highly intricate solution space [2]. Additionally, the non-linearity of these problems means that the relationships between variables are not straightforward or proportional, further complicating the optimization process. Moreover, multiple constraints, such as resource limitations, regulatory requirements, and environmental considerations, add layers of complexity to the problem [3]. To effectively tackle such challenges, there is a growing need for advanced optimization algorithms that can navigate these complexities with precision and efficiency. This is where hybrid optimization methods come into play. These methods represent a sophisticated approach that merges the strengths of various algorithms, each contributing its unique capabilities to the overall process [4]. For instance, hybrid methods may combine global optimization techniques, which are adept at exploring large and complex solution spaces, with local optimization techniques, which excel at fine-tuning solutions within a specific region of the search space. By integrating these approaches, hybrid optimization methods can offer a more balanced and robust solution strategy, addressing both the broad exploration of potential solutions and the detailed refinement of those solutions [5]. The power of hybrid optimization lies in its ability to enhance both performance and reliability. Performance is improved by combining different algorithms that can adapt to various aspects of the problem and avoid common pitfalls, such as becoming trapped in local optima [6]. Reliability is achieved by leveraging the complementary strengths of the algorithms, leading to more consistent and dependable results. This holistic approach not only improves the efficiency of finding optimal solutions but also ensures that the solutions are more applicable and practical in real-world scenarios. As engineering and technological fields continue to evolve, the role of hybrid optimization methods becomes increasingly crucial in addressing the sophisticated and dynamic nature of modern design problems [7]. In this paper, we introduce a hybrid optimization framework called JADEDO, designed to address both benchmark-driven and practical optimization challenges. JADEDO combines the strengths of two established metaheuristics, namely, (1) dandelion optimizer (DO): Inspired by the dispersal of dandelion seeds, DO employs a sequence of stages (dispersal, propagation, and competition) that collectively push the search process toward promising regions while maintaining the potential to escape local optima. (2) Adaptive differential evolution (JADE): Known for its adaptive mutation strategies and crossover operators, JADE dynamically adjusts parameter settings (e.g., mutation factor and crossover rate) to balance exploration and exploitation over the course of the search. By merging these two paradigms, JADEDO leverages DO's robust dispersal mechanisms to broaden global exploration, while JADE's adaptive operators refine local searches effectively. The resultant synergy endows JADEDO with heightened flexibility and robustness across different problem landscapes--unimodal, multimodal, and hybrid. To validate its performance, JADEDO is extensively benchmarked against a suite of well-known and cutting-edge metaheuristics using the IEEE CEC2022 test functions.