# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols
### TPSO-CR
#### 3.5.2 Ant Colony Optimization  
The Ant Colony Optimization (ACO) is defined by [62] as a novel nature-inspired metaheuristic for the solution of hard combinatorial optimization problems. The ACO algorithm originates from the behavior of ants which communicate with each other by using chemical deposits called pheromones. When ants move, they lay pheromones on the ground, and they receive the current strength of pheromone [29]. Figure 6 below presents the simple ACO functioning in which ants find the best path between a food source and the nest. The main idea of the ACO metaheuristic is to model the problem as a search for the best path by constructing a path-graph that represents the states of the problem [9]. Many works in the WSN field used clustering algorithms based on the ACO to improve the performance of sensor networks. Figure 6: Presentation of the Ant Colony Optimization (ACO) model. T-ANT  
T-ANT is a clustered data dissemination strategy based on the ant swarm behavior [63]. It promotes a uniform distribution of CHs, which subsequently enables substantial energy savings. The cluster formation is achieved by exploiting two swarm behaviors: foraging and brood sorting. During the cluster setup (CS) phase, a node checks to see whether or not it possesses an ant when the timer expires. If the node has an ant, it becomes CH and advertises its neighbors by broadcasting a message with its node ID. During the set-up phase, the election of CHs uses a swarm of ants in which an ant corresponds to a control message. The ant could travel into the network up to the sink as deep as restricted by its time-to-live (TTL) field. When an ant arrives at a node, the next node is randomly chosen. In order to evaluate the performance of T-ANT, comparison with The Time-Controlled Clustering Algorithm (TCCA) and a multihop version of LEACH called m-LEACH is achieved. The obtained results have shown that the average energy consumed per round in T-ANT is slightly better the other two protocols. However, concerning the number of alive nodes, T-ANT highly achieves the best number of alive nodes. EBAB  
The Energy Balanced Ant-Based Routing Protocol (EBAB) [64] is a new adaptive dynamic routing algorithm based on simple biological ants that explore the network and find routes. EBAB produces clusters of unequal sizes to balance energy consumption. Clusters closer to the BS have smaller cluster sizes in order to maximize the lifetime of the network. According to the signal sent by the BS, all the nodes calculate their distance from the BS based on the RSSI, and then nodes decide to compete if necessary to be the unique CH when they are in the same area. In order to form clusters, each CH sends message in a range to tell the other nodes. The nodes send \"ACK\" message to join the cluster. However, if the node receives more than one message, it will choose the optimal CH according the distance and the energy. The inter-cluster communications are performed by an ameliorated version of ACO in which CH generates initially a Forward Ants (FA). If the FA is not on the BS, it continues to the next step, nevertheless, if it is on the BS, it then activates the backward ants which will increase the pheromone value. The proposed algorithm EBAB is compared to the traditional clustering LEACH in terms of the network lifetime and the amount of data received by the BS. Results have shown that the lifetime of EBAB (number of alive nodes) is longer than the network of LEACH because of the balanced model of the proposed algorithm. Results have also revealed that the amount of data received by EBAB is 1.7 times higher than LEACH. ACO-C  
The authors of [65] proposed an energy aware protocol called Ant Colony Optimization for Clustering (ACO-C). ACO algorithms have been used for clustering problems in which N objects are assigned to K clusters based on the distance between each object and the middle of the cluster.