## 2 Related Work  
The authors of [1] proposed an optimization solution for the controller placement problem (CPP) called SA failure foresight capacitated controller placement problem (SA-FFCCPP). The authors of [2] proposed and implemented a greedy heuristic annealing simulation algorithm to solve the CPP. The first task is to decide where to place the controllers within a resource-limited network. The proposed algorithm determined that digital controllers require that all network elements be covered in an optimal manner. The primary criterion employed by the algorithm was to reduce the distance between all nodes and selected controllers. The authors of [3] presented a solution based on the simulation of an annealing algorithm, whereby feedback resulted in significant improvements, representing a specific implementation of resource planning with a focus on a resource mapping model. The authors of [4] used machine learning in various application scenarios for network control and optimization, for example, network traffic analysis, traffic forecasting, abnormal analysis, network simulation, and fault diagnosis. Path optimization algorithms include particle swarm algorithms, genetic algorithms, and SA algorithms. The authors of [5] decomposed the intractable offline problem into smaller cases, which were solved in an efficient manner online using an algorithm based on SA. Furthermore, they analyzed the need for frequent adjustment of the control plane and compared multiple control plane design options according to a new elasticity scale. The authors of [6] proposed and experimentally tested a method based on SA and quantitative annealing (QA), which surpassed other methods in terms of performance and synchronization cost. In addition, relative to integer linear programming (ILP), their proposed algorithm is significantly more scalable, making it applicable to large-scale networks. The authors of [7] proposed a multi-controller hierarchical deployment strategy for a space-air-ground integrated network for the sixth generation (6G SAGIN) of an SDN-based system. A multiple-propagation strategy was employed to determine the delay model of the network, the loading model of the SDN controller, and the loss value to be optimized based on the SA to search for the optimal solution space. The proposed strategy takes into account dynamic changes in network topology, as well as SDN controller imbalances, to improve network performance. The authors of [8] proposed a solution to improve throughput, the probability of link failure, and transparency on the southbound interface (SBI) for control-level synchronous transmission in a wireless controller mode (WCPP), whereby the southbound interface (SBI) depends on an unlicensed 4G LTE long-termevolution network. Two indicative solutions were considered: one based on SA and the other based on radiography. The authors of [9] evaluated the performance of Cuckoo-PC in comparison with SA and quantitative softening (QA) methods. Experiments showed that Cuckoo-PC surpasses both (SA) and QA in terms of network performance. In [10], a QoS service was developed in an avionics system operating in a heterogeneous wireless network environment by applying two algorithms--analytical hierarchy process (AHP-SA) and SA for synchronous weights and optimizing network selection (SA-SWNO)--to dynamically optimize the weighting factors of objective functions. The authors of [11] focused on C2C and S2C delays resulting from link failure and unreliability associated with the CPP. They used two algorithms--spectral clustering and ant colony (ACO)--to generate a versatile assignment plot for computing resources. They determined that the resource allocation algorithm achieved better performance than the ant colony algorithm, according to the criteria described above, and showed that SA diverged from the ideal neighborhood arrangement of the optimal local solution with a certain probability. The authors of [12] proposed SA based on enhanced mass peak density (DBSAA).