## 2 Related Studies
[60] applied a hyper-heuristic method based on the choice function selection mechanism to solve the multi-objective quadratic assignment problem (mQAP). The task of the choice function is to select the genetic operators that are LLHs during an iteration of the multi-objective evolutionary algorithm (MOEA) as the base method. The method was reported to perform quite well on 22 instances of mQAP after being benchmarked against other multi-objective optimization algorithms. Some of the approaches for solving a quadratic assignment problem (QAP) tend to favor the hybridizations of two or more algorithms. The study [61] hybridized quantum computing principles with an evolutionary algorithm to solve QAP while another study [62] integrated tabu search with a whale optimization algorithm. In [62], tabu search was employed to improve the solution constructed by the whale optimization algorithm. The study [63] hybridized an evolutionary algorithm called elite GA with a tabu search for solving 135 instances of the QAP from the well-known QAPLIB dataset. The authors reported that the hybrid method obtained the best-known solutions for 131 instances. [PERSON] et al. [64] applied a robust tabu search method to control the exploration-exploitation balance within an artificial bee colony. The resulting method was able to optimally solve 125 out of the 134 benchmarkinginstances of the problem. The agglutination of GA with Tabu (GA-Tabu) search has been recently published for solving the QAP [65] with a favorable comparative performance. Tabu search was hybridized with the evolutionary algorithm to solve the MAC [66]. The authors were able to find the best new solutions for 15 of 91 instances used to test their algorithm. [PERSON] et al. [67] proposed a binary artificial bee colony algorithm with a local search procedure to solve 24 instances of the MAC as reported in the literature. [PERSON] et al. [68] compared the performances of harmony search and two implementations of genetic algorithms, namely generational GA and steady-state GA, that were tested on 31 instances of the MAC. The study reported that the harmony search algorithm outperformed the two GA implementations. The Q-learning model-free reinforcement learning algorithm was proposed to solve some instances of the MAC [69]. The method utilizes a message-passing neural network (MPNN) to predict the Q-values of removing or adding vertices to solution subsets. Seven observations were configured to characterize the problem state during the training of the deep Q-network. Previous algorithms have used the same instances of the HyFlex framework for performance evaluation as follows: The FS-ILS with its variant NR-FS-ILS, based on the ILS, recorded a subdued performance on the extended HyFlex domains [24]. The AdapHH [20] is a state-of-the-art hyper-heuristic algorithm that outperformed both FS-ILS and NR-FS-ILS on the extended benchmarking test domains [24; 25]. It uses a relay hybridization technique to pair LLHs while solving a problem instance and emerged first in the CHeSC 2011 competition. The SSHH based selection hyper-heuristic (SSHH) based on the principles of the HMM was proposed in [70]. It attempts to automatically identify the optimal sequences of heuristics while optimizing the solution of a given instance of a problem domain. The SSHH was initially tested on the first six problem domains in the first version of the HyFlex framework to outperform the twenty CHeSC 2011 entries in the SAT, BP, and TSP problem domains. The algorithm recorded a favorable performance amongst several competitors on KP, QAP, and MAC [25]. The other methods include the EPH and two simple hyper-heuristics that choose LLHs randomly [24]. It is important to review the past research works on the HyFlex domains to identify gaps for further improvement. However, algorithms such as the SSHH, and sometimes AdapHH, that were designed to automatically generate heuristic sequences could benefit from the explicit separation of diversification and intensification found in ILS-based methods.