## 2 Related Work
Experiments were conducted on a real-world network topology to verify the effectiveness of the proposed algorithm for common placement problems, achieving nearly optimum performance in a shorter runtime. In [13], a heuristic update algorithm (HRIA) based on (SA) and a greedy algorithm was proposed to search for an approximate optimal solution. The authors of [14] proposed simulated double annealing; their results showed that the double-annealing algorithm outperformed the latest hybrid annealing and aggregation algorithm in terms of solution accuracy, with a small tradeoff in terms of runtime. The authors of [15] proposed an SDN controller deployment scheme based on a simulated solid genetic hybrid algorithm. This algorithm combines SA and the genetic algorithm. SA is used to quickly search for an improved solution and quickly find the optimal path. The authors of [16] introduced a strategy to address the CPP that protects against latency, potential link failure, and transparency in the case of an SBI wireless interface. They modelled the problem of locating wireless controllers in an SDN. To this end, the authors presented an indicative solution based on SA and genetic algorithms (GAs) that provides a fast and efficient solution. The authors of [17] suggested a hybrid of PSO and SA called HPSOSA for a controller flex mode to reduce the mean latency, as controller flex mode deals with the issue of node failure or link failure. The authors of [18] proposed a multi-objective hybrid harmony algorithm (MOHS-SA) to identify optimally Pareto-distributed solutions, for example, to reduce the total operating costs of facilities, optimize vehicle mileage, and reduce the cost of CO\({}_{2}\) emissions. The authors of [19] proposed a hybrid algorithm (HS-SA) that considers dynamic values of harmony memory (HMCR) and pitch modulation rate (PMR) using local optimization techniques for hybridization and probability based on SA. The authors of [20] also presented a hybridization simplification algorithm that combines HS and SA, known as HS-SA, for accurate and precise breast malignancy detection. The authors of [21] proposed a control system based on the integration of SDN and IoT in Smart City environments, based on application requirements. This control system detects when an emergency occurs and dynamically modifies the routes of normal and emergency urban traffic in order to reduce the time required for emergency resources to arrive at the emergency area, achieving an improvement ratio of 33%--from 26 ms to 17 ms. The proposed control system works through the application of priority and path-sharing avoidance via an alert treatment algorithm. An average time difference of 50% was achieved between the alternative path and the congested path. The authors of [22] proposed a dynamic QoS algorithm in an SDN to select the optimum path that ensures video QoS in order to optimize the quality of experience (QoE). The results demonstrate that the proposed method achieved improved viewing quality for a smart community environment and increased the overall network throughput. The authors of [23; 24] proposed a hybrid metaheuristic HSA-PSO algorithm to effectively deploy multiple controllers according to an MCSDN approach in order to reduce communication and propagation latency and improve throughput and reliability. Table 1 provides a summary of the weaknesses, limitations, and objectives of the surveyed literature, which can inform the research findings of the proposed algorithm (SA-MCSDN) in terms of various performance measures. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Study** & **Drawbacks and Weaknesses** & **Objective(s)** & **SA-MCSDN Approach** \\
**Ref./No** & **of Literature Review** & & \\ \hline \hline \multirow{8}{*}{[1]} & Focus only on latency, reducing worst-case latency between S2C, and reducing the execution time for large networks; failures and limitations on control capabilities are expected, and this model produces a near-perfect solution. & SA-FFCCPP-based optimized solution for CPP in SDN.