# An Optimization Routing Algorithm for Green Communication in Underground Mines
## 2 Related Work
### Genetic Algorithm  
Many studies have investigated heterogeneous networks based on GAs to reduce energy consumption and extend the lifetime of the network. In the Stable Election Protocol (SEP) [23], cluster heads are elected based on weighted probabilities that depend on the residual energies of sensor nodes. Then, based on categorizing the energy levels of sensor nodes, the Developed Distributed Energy-Efficient Clustering (DDEEC) [24] method was introduced to improve the SEP. The nodes with high energy levels are the \"advanced nodes\", and the cluster head is selected from these advanced nodes. The Threshold Sensitive Stable Election Protocol (TSSEP) [25] also improved the original SEP. In this approach, the energy levels of mobile nodes are divided into three groups according to the threshold intervals of the cluster head node. In addition, cluster heads are elected based on the proportion of their residual energy according to the Energy-Efficient Heterogeneous Clustering (EEHC) scheme [26] and the Efficient Three-Level Energy algorithm (ETLE) [27]. ETLE established three stable levels of energy to characterize node heterogeneity. Cluster head nodes are chosen based on these standards and solve the effect of node energy heterogeneity on the network lifetime. In the Hybrid Energy-Efficient Reactive protocol (HEER) [28], a cluster head is elected based on the ratio of the residual energy of nodes to the average energy level of the network. It chosen the cluster head nodes by using APIT (Approximate Point-In-Triangulation) to measure the distance and the energy consumption functions of heterogeneous nodes and solves the impact of heterogeneous energy of nodes on clustering. However, the clustering algorithms described above are only heterogeneous for the initial energy of the mobile node. Therefore, these algorithms are not suitable for a GCUM. The initial energy of mobile nodes differs, and the energy consumed by transmission varies due to differences in functionality. In other algorithms, connectivity also influences whether a mobile node is selected as a cluster head node, and the number of neighbor nodes around a mobile node affects connectivity. For example, a mobile node with many neighbor nodes is more likely to become a cluster head node. Low mobile node connectivity often means that the transmission of information is limited. Therefore, poorly-connected nodes are less likely to become cluster head nodes. When the clustering process begins, each mobile node begins to update its neighbor table. It concurrently broadcasts packets to the neighbor nodes and receives packets, thereby determining its own connectivity during this process. In [29], a cluster head node was elected primarily based on the degree of connectivity. In addition to the degree of connectivity of a node (i.e., the density of nodes), the number of neighbors is also used as a research criterion. Intuitively, if a sensor has more neighbors, it will have a higher probability of serving as a cluster head node. At the start of the clustering process, each node receives information from its neighborhood through broadcasted hello packages. In the Energy-Efficient Unequal Clustering (EEUC) method [30], the energy consumption of nodes, before clustering, is considered to determine the optimal cluster size. A two-stage GA is employed to determine the optimal interval of cluster size and derive the exact value from the interval. The proposed Circular Motion of Mobile-Sink with Varied Velocity Algorithm (CM2 SV2) balances the energy consumption ratio of cluster heads. These algorithms often focus on a single factor. Additionally, many intelligent algorithms use routing protocols based on clustering algorithms that can be applied to solve enhanced learning algorithms, ant colony algorithms, fuzzy logic algorithms, and GAs that are similar in highly complex and dynamic environments with multiple factors. In addition, many studies have proposed various clustering mechanisms, such as the Local Negotiated Clustering Algorithm (LNCA) [31].