# A Hybrid JADE-Sine Cosine Approach for Advanced Metaheuristic Optimization
## 2 Literature Review
Utilizing a combination of sine cosine optimization (SCO) and balloon effect identifier (BEI) algorithms, their method ensured stability under various conditions, demonstrating significant improvements in regulating the frequency and power of an islanded uG under variable load and system parameters uncertainties. [PERSON] et al. [28] investigated the effect of hybrid nanofluids on heat transfer around a vertical rod with cosine heat flux. By adding TiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\) nanoparticles to deionized water, they achieved significant temperature reduction and improved thermal performance in both turbulence and laminar flow regimes, particularly under varying pressures. [PERSON] and [PERSON] [29] proposed a hybrid binary SCA combined with ant-lion optimization (ALO) for feature selection problems. Their hybrid SCALO algorithm showed superior performance in eliminating inappropriate features and enhancing classification accuracy across diverse datasets. [PERSON] et al. [30] addressed mixed-integer nonlinear programming problems by presenting a hybrid improved sine and cosine algorithm. Their improved position update formula and combined mutation strategies significantly enhanced global and local search abilities, providing effective solutions for high-dimensional optimization problems. [PERSON] et al. [31] developed a hybrid compression method for NMR data using window averaging and DCT. Their WAC-DCT method effectively reduced inversion time while maintaining high compression rates, demonstrating superior performance compared to other compression methods. [PERSON] [32] proposed a transmission expansion planning model to avoid cascading failures in modern active power systems. By integrating the snake optimizer and sine cosine algorithm (SO-SCA), the model effectively minimized cascading outages and improved system resilience under different failure scenarios. [PERSON] et al. [33] introduced a hybrid optimization method combining SCA with the cuckoo search algorithm (CSA). Their hybrid modified sine cosine algorithm cuckoo search algorithm (HMSCACSA) demonstrated faster convergence and reduced computational time, outperforming other hybrid metaheuristics in various benchmark tests. [PERSON] et al. [34] proposed an optimized droop controller for energy management in stand-alone microgrids using a hybrid monarch butterfly and sine cosine algorithm. Their SCMBO method achieved optimal energy management solutions and maintained microgrid stability under various load conditions. [PERSON] and [PERSON] [35] hybridized the sine cosine algorithm (SCA) with an artificial bee colony (ABC) algorithm to address global optimization and image segmentation problems. Their SCABC algorithm enhanced exploration and exploitation capabilities, achieving better search results and image segmentation performance compared to classical ABC and other optimization algorithms. In recent years, metaheuristic algorithms have gained popularity in tackling optimization problems due to their flexibility and effectiveness in handling complex, nonlinear systems. The carnivorous plant algorithm (CPA), recently proposed as a population-based optimization approach, mimics the natural strategies of carnivorous plants to balance exploration and exploitation phases. An improved version, termed I-CPA, integrates a teaching factor strategy to enhance exploitation capabilities and stabilize results. The I-CPA has shown competitive performance in solving solar photovoltaic (PV) parameter identification problems, achieving lower root mean square error (RMSE) values compared to other metaheuristics [36]. Similarly, the tree seed algorithm (TSA) has been proposed to address continuous optimization issues by modeling the relationship between trees and seeds. To address limitations like local minima entrapment and loss of diversity, a multi-strategy tree seed algorithm (MS-TSA) was introduced, incorporating self-adaptive weighting, chaotic elite learning, and experience-based learning.