# A Hybrid JADE-Sine Cosine Approach for Advanced Metaheuristic Optimization
## 2 Literature Review  
In recent years, numerous hybrid optimization methods have been proposed to tackle complex engineering design problems, leveraging the strengths of different algorithms to enhance performance in terms of efficiency, accuracy, and robustness. [PERSON] et al. [16] introduced a hybrid approach integrating empirical wavelet transform (EWT) with a robust minimum variance random vector functional link network (RRVFLN), optimized using a sine cosine and Levy flight-based particle swarm optimization (PSOLVSC). This method demonstrated significant improvements in short-term solar power forecasting accuracy, showcasing the effectiveness of combining multiple optimization strategies to handle large and time-varying databases. Similarly, [PERSON] et al. [17] explored the optimization of construction duration and schedule robustness using a hybrid grey wolf optimizer (GWO) with the sine cosine algorithm (SCA). Their model improved convergence, diversity, accuracy, and distribution, proving its efficacy through a case study of an underground power station in China. Another notable study by [PERSON] et al. [18] proposed a hybrid optimization method combining SCA with Harris hawks optimization (HHO) to enhance exploration and exploitation capabilities, particularly for low and high-dimensional feature selection tasks. Their extensive experimental and statistical analyses confirmed the superior performance of the method in terms of search efficiency and convergence speed. Further extending the application of hybrid methods, [PERSON] et al. [20] developed a hybrid algorithm integrating GWO, opposition-based learning, SCA, and reinforcement learning for reliable scheduling and resource allocation in space debris tracking. This fusion algorithm demonstrated improved performance in managing allocation and scheduling problems, validated through real-world data. In the domain of biometric identification, [PERSON] et al. [21] proposed a hybrid model combining the discrete cosine transform (DCT) and deep learning for children's fingerprint identification. Their approach achieved a remarkable accuracy of 99% by integrating traditional and modern computational techniques. Similarly, [PERSON] et al. [22] introduced an adaptive sine cosine algorithm (ASCA) for optimizing PID controllers in hybrid renewable energy systems (HRES). By enhancing the traditional SCA with Levy flight distribution and adaptive operators, their method significantly improved the performance of HRES controllers under various operating conditions. [PERSON] and [PERSON] [23] addressed the heterogeneous fixed fleet vehicle routing problem using a hybrid approach combining SCA and particle swarm optimization (PSO). Their HSPS algorithm exhibited faster convergence rates and better solution quality compared to other hybrid algorithms. [PERSON] et al. [24] proposed an improved hybrid particle swarm optimizer with sine cosine acceleration coefficients (IH-PSO-SCAC) for transient electromagnetic inversion. Employing reverse learning strategies and sine cosine mapping, their method enhanced population diversity and global convergence, demonstrating superior stability and accuracy in geophysical applications. [PERSON] et al. [25] presented a robust digital watermarking system based on a combination of DCT, discrete wavelet transform (DWT), and singular value decomposition (SVD). This hybrid approach ensured improved robustness, high imperceptibility, and enhanced security, making it effective against various attacks. [PERSON] et al. [26] introduced a particle swarm sine cosine algorithm (PSSCA) for optimizing retaining structures under seismic loads. Their hybrid algorithm leveraged the dynamic behavior of sine and cosine functions to achieve faster convergence and better accuracy, outperforming other competitive algorithms in seismic optimization tasks. [PERSON] et al. [27] proposed an adaptive load frequency control (LFC) for isolated microgrids (uGs) that includes PV systems and electric vehicles (EVs).