## 2 Literature Review
### State of the Art
The results illustrated that NSGA-II outperformed SPEA2 in optimizing the CLOP problem. In a later study, [PERSON] et al. [11] have proposed a multi-objective optimization problem of WSN based on scheduling algorithm to control the node density. Their objective is to achieve the maximum coverage with a good life-time of the network. This algorithm is used to schedule the randomly deployed active nodes, in which, if any failure occurs the optimization algorithm will rearrange the network unless all nodes have lost their connectivity or energy. [PERSON] et al., have compared between a set of algorithms to get the maximum coverage and minimum energy consumption. The first algorithm is Multi-objective Evolutionary Algorithm Based on Decomposition (MOEA/D), which is a GA framework that decomposes a multi-objective problem to a set of single objective problems. The second algorithm is NSGA-II. The results showed that MOEA/D outperformed NSGA-II in finding the optimal results for two objective functions. However, the node selection problem of WSN is not discussed in this study. For this reason, [PERSON] et al. [12] have proposed selecting a set of nodes rather than utilize all the nodes in the network, which will increase the network lifetime by reducing the power consumption in the whole network. The optimization algorithms such as GA, Convex Optimization Algorithms, Binary Particle Swarm Optimization (BPSO) and PSO-Cyclic Shift Population (CSP) algorithm are used to evaluate this problem. The results showed that BPSO outperformed the other algorithms in finding the optimal number of selected sensors. Later, [PERSON] et al. [13] have proposed an improvement on Multi-Objective Particle Swarm Optimization (MOPSO) using crowding factor and archive method. This algorithm is used to optimize two conflicting features in WSNs such as maximizing the node coverage and minimizing the energy consumption for each node. The efficiency of their algorithm was evaluated and compared with the original version of MOPSO. The simulation parameters were topology size equal to 20 \(\times\) 20 m, number of sensors equal to 40 sensors. The parameter settings of the algorithms were the number of particles equal to 30, and the number of iterations equal to 300. The experimental results showed that the improved version of MOPSO outperformed MOPSO in terms of maximizing the network coverage and minimizing the network energy consumption. [PERSON] et al. [14] proposed a multi-objective optimization modeling of WSN network using NSGA-II and MOEA/D. Their work finds an efficient routing to the sink node to maximize the network coverage and minimize the energy consumption for each node. The simulation parameters were topology area equal to 100 \(\times\) 100, number of sensors equal to 25. The parameter settings of the algorithms were the crossover probability equal to 0.6, mutation probability equal to 0.03, population size equal to 50, and the maximum number of generations equal to 50. The results showed that NSGA-II outperformed MOEA/D in both minimizing the energy consumption and maximizing the node coverage. However, the features that are affected by the interference resources are not examined. For this reason, [PERSON] et al. [15] have discussed the challenges that faced by 2.4 GHz WSN. They have discussed a set of multi-objective features that are affected by the interference from other devices that operate on the same band such as microwave oven and Wi-Fi router. These features are packet throughput and energy efficiency. They also maximized these features using three optimization algorithms such as NSGA-II, OMOPSO, and SPEA2. These features were evaluated by changing the distances between both interference source and receiver, and also between transmitter and receiver. The results showed that the NSGA-II outperformed both SPEA2 and OMOPSO in maximizing the previously mentioned features.