## 2 Related Works  
Meta-heuristic algorithms have garnered significant attention because of their simplicity of implementation, high convergence rates, and robust capabilities in solving complex optimization problems, including non-linear, mixed-variable, and constrained problems [25]. Typically, these algorithms begin with a randomly generated population of solutions, which are iteratively refined. What differentiates one meta-heuristic algorithm from another is the method used to update or modify these candidate solutions during the optimization process. In the context of Cognitive Dynamic Environments (CDEs), meta-heuristics have proven effective for dynamic parameter adaptation, with over two decades of research dedicated to this field. CDE optimization has been explored using various meta-heuristic algorithms, such as Genetic Algorithms (GAs) [26], PSO [27], Artificial Bee Colony (ABC) algorithms [28], Ant Colony Optimization (ACO) [29], Simulated Annealing (SA), Biogeography-Based Optimization (BBO) [30], and Cat Swarm Optimization (CSO). This work specifically focuses on leveraging these techniques to enhance parameter adaptation in CDEs. For instance, [PERSON] et al. [26] proposed a GA-based approach to optimize transmission parameters in both single- and multi-carrier CR systems. Their method used a weighted sum strategy to simultaneously optimize multiple fitness functions, adjusting parameters such as power levels and modulation schemes. However, GA often suffered from local optima traps and required around 500 iterations to reach convergence. [PERSON] et al. [27] employed PSO to adapt parameters in a multi-carrier cognitive radio setup under a Multi-Objective Optimization (MOO) framework. The PSO algorithm showed superior performance over GA in terms of fitness values and solution stability across four operational modes. A comparative study by [PERSON] et al. [28] evaluated GA, PSO, and ABC algorithms in optimizing CDEs across three transmission modes, while respecting interference constraints: low-power, multimedia, and emergency. Their results showed that ABC outperformed both GA and PSO in terms of average fitness and computation time. [PERSON] et al. [29] also introduced a Mutated Ant Colony Optimization (MACO) algorithm designed for CDE optimization involving 10 subcarriers. The mutation mechanism in MACO helped escape local minima and yielded superior performance, particularly under a 0.9 pheromone evaporation rate, outperforming both GA and traditional ACO. [PERSON] et al. [30] proposed an SA-based method to optimize transmission parameters while meeting stringent QoS constraints like minimizing power, reducing bit error rates, and improving throughput and spectral efficiency. Although the SA model produced better fitness values than GA, it required a longer computation time, making it less practical for real-time scenarios. Building on these efforts, [PERSON] et al. [25] introduced a BBO strategy for single-carrier CR systems. BBO uses immigration and emigration operators to balance exploration and exploitation. Comparative results demonstrated that BBO achieved higher fitness than GA in a variety of settings. Lastly, [PERSON] et al. [25] introduced a Modified Spider Monkey Optimization algorithm tailored for spectrum scheduling, aiming to improve energy efficiency. Their technique utilized a revised round-robin scheduling approach and evaluated multiple performance metrics, such as false alarm probability, system throughput, and transmission success rate. The method outperformed several state-of-the-art techniques across these metrics. In summary, the application of meta-heuristic algorithms in CDE has proven to be a powerful approach for optimizing transmission parameters under dynamic spectrum conditions. These methods offer significant advantages in handling multi-objective, non-linear, and constrained optimization problems inherent in CRNs.