## 2 Related Works
Their main goal was to maximize the coverage of all targets (on the basis of their coverage requirements) by activating a minimal number of sensors. They solved this heterogeneous coverage problem by modifying the formulation of an existing integer linear programming method for the single and \(k\)-coverage MCMS problems. They also presented a sensor-oriented greedy algorithm to obtain an approximate solution of the formulated problem. [PERSON] et al. [39] successfully addressed the \(k\)-coverage problem by determining the orientation of a minimum number of directional sensors in which each target is monitored at least \(k\) times, with a learning automata-based algorithm. The sensor nodes used in these studies comprise a fixed number of sensors with fixed orientations. However, many sensor nodes can rotate. [PERSON] and [PERSON] [40] addressed the maximum coverage with rotatable angles (MCRA) problem; they maximized the coverage rate of targets and minimized the total rotation (in degrees) of the sensors. They took advantage of the ability to adjust the working direction and presented two centralized greedy algorithms for the MCRA problem. In both greedy methods, the weights of sectors were used to select the appropriate working direction to which the sensors should rotate to cover more targets. [PERSON] and [PERSON] [41] redefined the MCMS problem for a DSN comprising rotatable sensors. They proposed a greedy algorithm that used a minimum number of sectors to cover targets within the sensing range of the sensors. [PERSON] and [PERSON] [16] also addressed the MCMS problem in a DSN with rotatable sensors. They proposed a GA for scheduling the orientation of active sensors to obtain a higher coverage rate of targets compared with that of other greedy algorithms. We considered the target coverage problem with rotatable sensors. The goal aims to determine an appropriate orientation schedule in which each active sensor rotates to the scheduled orientation such that (1) the total number of covered targets is maximized and (2) the total number of active sensors is minimized. We focused on presenting different metaheuristic algorithms, including GA, DPSO, and the hybridization of DPSO and GA, for the problem due to its complexity. The hybridization of PSO and GA approaches has been applied to many applications in the last two decades [17; 18; 19; 20; 21; 22; 23; 24]. [PERSON] et al. [17] proposed a hybrid PSO and GA algorithm by taking the population of one algorithm when the algorithm fails to improve, and using it as the new population of the other algorithm. [PERSON] et al. [18] proposed a hybrid algorithm, namely PSO-GA-series-hybrid evolutionary algorithm (PGSHEA), which is to integrate PSO and GA methods in series. The PSO algorithm is terminated after a certain number of iterations and the best particles are selected and encoded into chromosomes to constitute the population for GA algorithm. The GA algorithm is terminated after a specific number of iterations and the best solutions of GA are transmitted back to the PSO populations. [PERSON] et al. [19] proposed a PSO-GA-based hybrid evolutionary algorithm (HEA) for solving unconstrained and constrained optimization problems. Their proposed evolution strategy is divided into two stages in which the evolution process is accelerated by using PSO and diversity is maintained by using GA. [PERSON] et al. [20] proposed a fuzzy approach in the PSO-GA hybridization. They used several fuzzy rules to determine whether to consider GA or PSO particles and changes their parameters or to take a decision. [PERSON] and [PERSON] [21] proposed a feature selection method by hybridizing GA and PSO. The proposed method was confirmed to be able to automatically select the most informative features within an acceptable processing time without requiring the users to set the number of desired features beforehand. [PERSON] and [PERSON] [22] introduced a combination of PSO and GA approach to classify softwaremodules as fault-prone or not using object-oriented metrics. [PERSON] et al.