# A Hybrid JADE-Sine Cosine Approach for Advanced Metaheuristic Optimization
## 1 Introduction
By leveraging the complementary strengths of multiple algorithms, hybrid optimization methods aim to enhance the efficiency, accuracy, and robustness of the search process [13]. This research paper introduces a novel hybrid approach, integrating the sine cosine algorithm (SCA) with the advanced evolutionary algorithm known as JADE (adaptive differential evolution with an optional external archive), to effectively solve such intricate problems. The SCA is renowned for its simplicity and effectiveness in exploring the search space, utilizing mathematical sine and cosine functions to update the positions of solutions. This ensures a diverse and widespread exploration, making SCA adept at avoiding local optima and promoting global exploration in the initial stages of the optimization process [14]. Conversely, JADE incorporates adaptive mechanisms and an external archive to enhance its performance. It adjusts its mutation and crossover parameters dynamically, maintaining a balance between exploration and exploitation. JADE's external archive stores successful solutions, guiding future search directions and improving convergence speed. Its robust adaptive mechanisms and effective local search capabilities make it highly suitable for fine-tuning solutions and converging towards the global optimum in the later stages of the optimization process [15]. In recent years, hybrid optimization methods have gained considerable attention due to their effectiveness in solving complex engineering design problems. For instance, [PERSON] et al. [16] introduced a hybrid approach combining the empirical wavelet transform with sine cosine and particle swarm optimization for enhanced solar power forecasting, achieving significant improvements in accuracy. Similarly, [PERSON] et al. [17] applied a hybrid grey wolf optimizer and sine cosine algorithm for construction project scheduling, demonstrating superior convergence and solution quality. These studies, along with others like [PERSON] et al. [18], who proposed a hybrid sine cosine algorithm with Harris hawks optimization for feature selection, showcase the growing interest in hybrid techniques. These examples highlight how hybrid approaches are increasingly utilized to overcome the limitations of single algorithms in addressing high-dimensional, multimodal, and non-linear problems commonly encountered in engineering design. By leveraging the complementary strengths of different algorithms, hybrid methods provide improved exploration, exploitation, and convergence characteristics, making them well-suited for solving real-world engineering challenges. The motivation for developing this hybrid approach lies in overcoming the inherent limitations of individual algorithms. The SCA is particularly adept at global exploration due to its mathematical design, which ensures a diverse set of candidate solutions and avoids premature convergence. However, it struggles with fine-tuning solutions during the later stages of optimization. Conversely, JADE excels in local search capabilities and adaptability but may require enhancement in its initial exploration phase. By combining these algorithms, we leverage their strengths and mitigate their weaknesses, leading to a more efficient and effective optimization process [19].