## 3 Related Work  
In this section, we review existing approaches that utilize nature-inspired algorithms for coverage in wireless sensor networks (WSNs). While ant colony optimization (ACO) and the binary ant colony algorithm (BACA) have gained attention, these methods have not been extensively researched in the context of coverage. [PERSON] [1] proposed a survey paper that discussed the ant colony optimization method and the lion optimization method as nature-inspired algorithms to achieve coverage in WSNs. [PERSON], [PERSON], [PERSON], and [PERSON] [2] addressed the ant colony optimization problem to cover an area. Ants chose their path based on probabilistic rules, such as the pheromone trails, and they ensured connectivity among sensors. They introduced penalties if a sensor was not covered by at least one other sensor. [PERSON], [PERSON], and [PERSON] [3] proposed an improved ant colony algorithm for sensor deployment in real sensor network systems. This paper made modifications to the convergence strategy and the ant state transition rule by proposing an improved ant colony algorithm known as EasiDesign. EasiDesign was proven to be more effective than the traditional ant colony algorithm. [PERSON] and [PERSON] [4] proposed a fast-spanning ant colony optimization for mobile robot cover path planning. Their methodinvolved ants with higher velocities finding destinations or obstacles faster and keeping lower-velocity ants informed by communicating such information via pheromone paths. Additionally, they employed coverage path planning (CPP), where robots would plan a path to cover all the free cells on an occupancy grid map. [PERSON] and [PERSON] [5] presented an advance in image interpolation based on the ant colony algorithm for high-resolution image scaling. Their work focused on leveraging the ant colony algorithm to improve image scaling by interpolating missing pixels. [PERSON], [PERSON], [PERSON], and [PERSON] [6] proposed a self-deployment algorithm for maintaining maximum coverage and connectivity in underwater sensor networks based on ant colony optimization. They carried out a greedy strategy and improved path selection probability by using a pheromone update system. [PERSON] [7] applied the ant colony optimization (ACO) algorithm in a realistic 3D environment by making modifications to the standard ACO algorithm. By making these adjustments, the algorithm demonstrated improved performance in solving optimization problems in the given environment. [PERSON] [8] proposed a hybrid ant colony algorithm, which combines the strong adaptability of the ant colony algorithm and the high convergence of the genetic algorithm to achieve network coverage and prolong the network lifetime. [PERSON] and [PERSON] [9] introduced a new feature selection algorithm based on binary ant colony optimization. This paper addressed an advanced binary ant colony optimization (ABACO) that treats features as nodes in a graph model, where each feature has two nodes representing selection and deselection. Ants traverse the graph and select nodes using the ant colony algorithm, resulting in a binary vector that represents a possible solution. [PERSON], [PERSON], and [PERSON] [10] proposed an improved genetic algorithm and the binary ant colony algorithm to achieve coverage. Their work incorporates enhancements to the genetic algorithm and the binary ant colony algorithm to find the local optimization of the problem of coverage. [PERSON], [PERSON], and [PERSON] [11] proposed a binary ant algorithm (BAA) to solve the problem of dynamic optimization. BAA acts by building pheromone maps over a graph of possible traits representing pseudo-solutions of increasing quality to a specific optimization problem. Our proposed approach in this paper is different from these related works. Specifically, this work takes into account two search optimization algorithms known as hill climbing (HC) and simulated annealing (SA) and compares the effectiveness between the two with the binary ant colony algorithm.