## 4 Swarm Hyper-heuristic Algorithm  
### Swarm Hyper-heuristic Algorithm  
As shown in Figure 4, the hyper-heuristic algorithm is an advanced type of intelligent computing method appearing in the early 2000 s, which provides a high-level strategy (HLS) that manipulates or manages a set of low-level heuristics (LLH) to improve the searching efficiency and performance. The hyper-heuristic algorithm works in a higher abstraction layer than the meta-heuristic algorithm, and the hyper-heuristic algorithm selects which low-level heuristic method to use at a particular moment through state of the problem solving [36]. Swarm hyper-heuristics algorithm searches a kind of good solution to the problem rather than the best solution of the problem directly, which is independent of the specific problem domain or background issues.  
The swarm intelligence, derived from the observation of insect swarms in nature, is the behavior characteristics exhibited by swarm organisms through cooperation. Swarm intelligence is an increasing focus in the field of metaheuristic algorithms, and the efficiency, and effectivity in solving complex problems has drawn much attention in recent years. However, some scholars believe that these similar algorithms lack novelty. Some algorithms have similar operations, by taking the different names and simulating the characteristics of another natural organism. The performance of the metaheuristic algorithm depends on how the algorithm balances two basic search mechanisms, intensification and diversification. The intensification gets the algorithm to perform a detailed search in the local, and the diversification prevents the solution from entering the local optimum prematurely. These two basic mechanisms include a variety of group operations. For example, fireflies' movements according to the light intensity and lightness in the firefly algorithm are intensification, and the random movement of fireflies is diversification.  
Figure 4: Hyper-heuristic framework and composition.  
Therefore, [PERSON] et al. [37] proposed a swarm hyper-heuristic algorithm framework, trying to integrate the swarm meta-heuristic algorithm with a general hyper-heuristic framework, where the updating operators were recognized as low-level heuristics and guided by a high-level hyper-heuristic. Different learning methods are used to determine the intensified and diversified behavior of the algorithm. According to whether or not to learn, it can be divided into three categories, no learning SHH1, offline learning SHH2, and online learning SHH3. According to the conclusion of [37], the performance of the online learning method SHH3 was better than the other methods.  
The swarm-based meta-heuristic algorithm is a swarm-based algorithm, in which the individual in the swarm interacts with each other through different update operators and achieves their own updates. The efficiency and effectivity of swarm hyper-heuristic algorithms are determined by two steps, including the heuristic selection and the heuristic generation. The heuristic selection is the method for choosing or selecting the appropriate heuristic at each iteration, while the heuristic generation is the procedure that generates various heuristics [36].