# Automated Generation of Hybrid Metaheuristics Using Learning-to-Rank
## 2 Related Work  
### Metaheuristic Algorithms and Optimization  
Metaheuristics have been widely applied to address complex problems across diverse domains, such as engineering design, combinatorial tasks, and multi-objective scenarios [4]. Although these algorithms possess strong search capabilities, as the scale and complexity of problems continue to grow, traditional single algorithms increasingly exhibit issues such as vulnerability to local optima, sluggish convergence, and high sensitivity to parameter tuning. Consequently, improving metaheuristic algorithms, particularly through algorithm integration and hybrid strategies to enhance their performance, has become a prominent research focus. To overcome the limitations of traditional approaches, researchers have proposed a variety of improvement strategies. [PERSON] et al. conducted a systematic review of current mainstream metaheuristic algorithms and their variants, highlighting that single algorithms often struggle with high-dimensional and non-convex optimization problems and that algorithm hybridization has become one of the effective means to enhance algorithm performance [4]. [PERSON] [14] indicated that hybrid metaheuristics, as an important future direction for optimization algorithms, is capable of enhancing solution diversity while simultaneously lowering the likelihood of local extrema. [PERSON] et al. [5] combined a Genetic Algorithm with a simulated annealing algorithm to propose a search-based fault localization method. By transforming the fault localization problem into a search problem for optimal modeling combinations, they achieved heuristic approximations of near-optimal solutions. [PERSON] et al. [15] proposed a Quality-of-Service (QoS) characterization model for cloud manufacturing services, as well as an QoS-aware service creation approach using Genetic Algorithms. They presented a new approach [16] that combines the hawk strategy with an enhanced Whale Optimization Algorithm to improve the QoS composition solutions. To address the limitations of the WOA, [PERSON] et al. [6] introduced the eswOA to reconcile exploration and exploitation. Empirical findings demonstrated that the eswOA achieves superior performance in locating globally optimal solutions. Furthermore, the egolden-SWOA [17] enhances population diversity via an elite opposition-based learning technique and uses the golden ratio to optimize the search process, successfully balancing global exploration and local exploitation capabilities. [PERSON] and [PERSON] conducted a systematic review of the emerging Dwarf Mongoose Optimization Algorithm (DMOA), discussing its latest variants, hybrid strategies, and application trends over the past three years, and emphasized that algorithm hybridization can effectively improve search efficiency and global convergence [7]. Overall, through continuous algorithmic optimization and diversified integration strategies, metaheuristic algorithms have achieved remarkable progress in addressing complicated optimization issues. Whether by dynamically combining multiple algorithms or harmonizing exploration and exploitation, these approaches have effectively enhanced the performance of individual metaheuristic algorithms and broadened their application boundaries. However, despite these advancements, most current algorithm composition methods still rely on empirical design and static configurations, lacking flexible adaptive regulation and intelligent combination capabilities. As problem scales continue to expand, strategies that rely solely on manual design are increasingly insufficient to fully unleash the potential of these algorithms. Against this backdrop, hyper-heuristic approaches and automated algorithm composition frameworks have emerged as important research directions for achieving intelligent scheduling and automatic evolution of algorithms.