## 3 Analysis of Literature Resources
### Hybrid Metaheuristics  
One particularly effective hybridization strategy involves integrating single-state metaheuristics, such as Tabu Search or Simulated Annealing, with population-based methods, such as GAs or Particle Swarm Optimization. While single-state methods excel at intensifying the search around promising areas of the solution space (exploitation), population-based algorithms provide greater exploration capabilities across the global search space. By hybridizing these approaches, researchers and practitioners can design optimization algorithms that achieve a more balanced search process, improving convergence speed, solution quality, and robustness. This section explores some successful hybrid metaheuristics for addressing NP-hard scheduling and optimization problems. Tabu Search (TS), known for its effective use of short-term and long-term memory strategies, has proven highly capable in tackling NP-hard problems. This strength has led to its frequent integration into hybrid metaheuristic algorithms, where it complements other optimization techniques. A notable example is its hybridization with ACO, commonly referred to as ACTS. This hybrid algorithm ACTS combines the global search capabilities of ACO with the local refinement power of TS, resulting in a more robust optimization method. The ACTS algorithm builds upon the classical ACO framework, enhancing it with TS features to improve performance in solving both discrete optimization problems--such as identifying shorter paths in graphs--and more complex continuous problems with multiple objectives. A critical parameter in ACTS is the number of colony members: while increasing the number can improve solution quality, it also significantly raises computational demands. In ACTS, the total system cost, used as the objective function, determines the amount of pheromone deposited by each colony member. A higher objective function value leads to more pheromone being spread, increasing the probability that the corresponding path will be selected in subsequent iterations. This mechanism helps ACTS deliver solutions with lower variance, indicating greater reliability and consistency. Generally, ACTS outperforms traditional Branch and Bound (B&B) algorithms, especially in large-scale problems where B&B approaches struggle to produce feasible solutions. Unlike B&B, ACTS can escape local optima effectively due to its memory structures, consistently producing superior solutions within the same number of iterations. However, while ACTS generally offers better performance, its convergence speed is not always guaranteed [68]. Moreover, ACO is also involved in another hybrid metaheuristic, where it is combined with GA, a single-state metaheuristic technique, to leverage the strengths of both approaches in solving complex optimization problems. Through a comparative analysis of both GA and ACO, it emerged that employing a hybrid model named Genetic Ant Colony (GA + ACO) allows for the combination of the strengths of both metaheuristic approaches, achieving better scheduling outcomes in terms of robustness and adaptability than either method used independently [63]. GAs are renowned for their robust global search capabilities, effectively exploring vast solution spaces to identify optimal solutions. However, they often struggle with complex problems due to their tendency to become trapped in local optima and the inefficiency arising from redundant iterations when nearing a solution. This limitation stems from GA's reliance on population-based search mechanisms without incorporating sufficient feedback mechanisms to guide the search process effectively [69]. This is unlike ACO which, inspired by the foraging behavior of ants, excels in adaptive search strategies. Despite having underdeveloped vision, ants can find the shortest path from a food source to their nest by following pheromone trails, which are updated dynamically based on the quality of the path.