# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 6 Recent Advancements in Clustering Optimization Algorithms
### Meta-Heuristic
#### 6.1.2 Swarm Intelligence Algorithms
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**Ref** & **Algorithm** & **Optimization Methods** & **Objective** & **Optimization Process** & **Matrices** & **Simulation** \\ \hline \multirow{4}{*}{[75]} & \multirow{4}{*}{ABC-SD} & Cluster-based routing protocol using the ABD algorithm. Formulation of the clustering problem as linear programming & \multirow{4}{*}{Energy consumptionCost-based Function} & Proposed exploitation of the nature-inspired search features of the ABC meta-heuristic for building low-power clusters and selecting CHs ICWAQ exploited the fast and efficient ABC algorithm search mechanism to optimize node clustering during CH selection to define the routing paths & \multirow{4}{*}{N/A} \\  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \cline{4-5}  & & & & & \\ \hline \hline \end{tabular}
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Table 4: Comparison of studies involving swarm intelligence. * Bee-sensor-C: An energy-efficient and scalable multipath routing protocol Ref. [77] introduced Bee-Sensor-C, an extension of the Bee-Sensor system (C stands for cluster); it is an energy-efficient protocol for multipath routing that depends on the foraging pattern and dynamic clustering of the bee swarm. It ensured balanced network energy usage by adopting an enhanced multipath construction strategy that minimized routing overheads and improved network performance and scalability. It employed a dynamic clustering technique for energy usage reduction and service life improvement. The HiveHeader was introduced in this protocol, in addition to the parameters used in the Bee-Sensor protocol. Cluster formation is the first phase of the Bee-Sensor-C; in this phase, the HiveHeader in the hive becomes the CH node upon detecting an event within a given area. In this way, the nodes within the event area are activated, followed by an estimation of the specific perceived attributes that would allow nodes with the event details to join the cluster. Multipath construction is the second phase of the system, wherein the CH propagates data to the BS; here, the CH scouts for the ideal node foragers for the existing multipath in the cluster. The transmission stage is the final phase and occurs upon the arrival of the backward scout at the CH with a specific path identity, followed by recruitment of the foragers using the bee waggle dance. The proposed approach was evaluated under two different network scenarios with varying nodes, and energy consumption and packet delivery rate were compared with Bee-Sensor, IEEABR, and FF-Ant. * TPSO-CR: Two-tier particle swarm optimization protocol for clustering and routing Ref. [78] proposed a two-tier PSO for clustering and routing. TPSO-CR is a protocol that relies on a clustering scheme for optimal CH selection; the protocol ensured efficient energy usage and improved network coverage and transmission reliability. The development of the routing algorithm was based on a novel particle encoding scheme and fitness function for finding the optimal path to connect the CHs to the BS. TPSO-CR is centralized and relies on information received by the BS, similarly to PSO-HC. The BS computes the average energy level of the nodes, so that only nodes with an energy level higher than the average can be considered as CHs for each round. This stage ensures that only nodes with sufficient energy to act as CHs are selected.