## 2 Literature Review
### State of the Art  
In this section, we will summarize a few studies that use a wide variety of heuristic-based algorithms to optimize different types of network features and then get an optimal QoS of the network [PERSON] et al. [6] have proposed a new algorithm called Energy-efficient Coverage Control Algorithm (ECCA) that works based on NSGA-II. This algorithm is used to optimize two conflict network features such as maximizing the network coverage and minimizing the network energy consumption. They have conducted different experiments to test the performance of this algorithm. In the first experiment, a total of 100 nodes was used to cover a topology size of 100 \(\times\) 100 m whereas in the second experiment, a total of 200 nodes was used to cover a topology size of 200 \(\times\) 200 m. Different test scenarios have been applied by changing the number of generations for each algorithm from 10 to 200 generations. The results showed that the algorithm is efficient in providing a good coverage with less energy consumption. [PERSON] et al. [7] have discussed a set of network features that affect QoS in the WSN to maximize the network lifetime and minimize task execution time. [PERSON] et al. proposed a modified version of Binary Particle Swarm Optimization (MBPSO) and compared it with two different algorithms such as Binary Particle Swarm Optimization (BPSO) and Genetic Algorithm (GA). The network features were tested by varying the number of nodes from 0 to 60 nodes in a topology size equal to 500 \(\times\) 500 and number of execution tasks for each node from 0 to 10 tasks. The results showed that MBPSO outperformed the other algorithms in term of optimizing the previously mentioned features. However, the coverage feature is not evaluated. For this reason, [PERSON] et al. [8] have discussed the coverage problem of WSN. Particle swarm optimization algorithm (PSO) is used to maximize the network coverage based on the distance between nodes in the topology area of 50 \(\times\) 50 m. They conducted different tests by changing the number of nodes up to 80 nodes. The results showed that the best coverage for 50 \(\times\) 50 area is when the number of nodes is 40 nodes. However, network end-to-end delay and energy consumption are not tested in this study. The tests are important because a longer distance between nodes will increase the network delay, thus, increase the number of dropped packets in the network. The retransmission of these packets will consume more power consumption and time. In a different study, [PERSON] et al. [9] have discussed the challenges in WSNs. A very important issue in WSN is network coverage, which is used to determine the optimal number of nodes that can cover all parts of the topology. They used two algorithms to maximize the network coverage and minimize the energy consumption. These algorithms are Optimal Geography Density Control (OGDC) and NSGA-II. Different tests were performed to find the optimal coverage ratio in a topology size equal to 100 \(\times\) 100. The parameter settings of the algorithm were population size equal to 100, crossover rate equal to 0.9, and maximum iterations of the algorithm equal to 250. The Pareto-front figures showed the previous objectives under changing the number of nodes from 0 to 400 nodes. Furthermore, The results showed that the NSGA-II outperformed OGDC, which used 210 nodes to cover the topology while the OGDC algorithm used 327 nodes to cover the same topology area. [PERSON] et al. [10] further discussed a Coverage and Lifetime Optimization (CLOP) problem of WSNs. They optimized two features for CLOP problems such as maximizing coverage and minimizing network energy consumption. [PERSON] et al. used two algorithms to optimize these features, including NSGA-II and SPEA2. The experiments were repeated 10 times by changing the population size for each algorithm from 300 to 5000 and the number of evaluations from 50,000 to 500,000. Moreover, the numbers of nodes were changed from 5 to 20 nodes.