# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 6 Recent Advancements in Clustering Optimization Algorithms
### Hybrid Techniques
#### 6.3.1 Hybrid Meta-Heuristics  
Ref. [101] reported using a hybrid technique based on an improved GA and a binary ACO to achieve optimal coverage, reduce data redundancy, and optimize the multi-objective function by determining the least number of sensors required. The role of the ABC was to extend the performance of the WSN and achieve better exploration and exploitation during CH selection [102]. For the improved ABC, the goal was to obtain optimal CHs for WSNs [103] and find the shortest routing path [104]. The fractional Grasshopper Optimization Algorithm (Fractional-GOA) was utilized to reduce energy usage by implementing sleep/wake scheduling in the nodes [105]. The optimal solution is considered the solution that offers the maximum fitness value and is the considered solution for sensor activation in the Fractional-GOA. We provide a general overview of the characteristics of hybrid meta-heuristic techniques. Table 6 summarizes these studies, investigated using different parameters. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Ref** & **Algorithm** & **Optimization Methods** & **Objective** & \begin{tabular}{c} **Optimization** \\ **objectives** \\ \end{tabular} & **Optimization Process** & **Matrices** & **Simulation** \\ \hline \multirow{5}{*}{[84]} & \multirow{5}{*}{HSA and PSO} & The hybrid HSA-PSO allows & & & & \
\begin{tabular}{c} The hybrid approach \\ makes use of the high \\ searching efficiency of \\ HKS combined with the \\ dynamic nature of PSO \\ \end{tabular} & \multirow{5}{*}{
\begin{tabular}{c} Number of live \\ nodes, number \\ of dead nodes, \\ throughput and \\ residual energy \\ \end{tabular} } & \multirow{5}{*}{MATLAB} \\  & & & & & & \\  & & & & & \\ \cline{4-7}  & & & & & & \\ \cline{4-7}  & & & & & & \\ \cline{4-7}  & & & & & & \\ \cline{4-7}  & & & & & & \\ \cline{4-7}  & & & & & & \\ \cline{4-7}  & & & & & & \\ \cline{4-7}  & & & & & & \\ \cline{4-7}  & & & & & & \\ \cline{4-7}  & & & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of studies involving hybrid meta-heuristic techniques. HAS-PSO: Hybrid HSA and PSO algorithm for energy-efficient cluster head selection  
In Ref. [84], the HAS-PSO algorithm was developed for CH selection based on the residual energy and distance to ensure better search efficiency and convergence. The first step in the has-PSO is to select the network parameters and assign values to the velocity, particle fitness function, and hybrid matrix. These parameters describe the Particle Harmony Memory (PHM) for the generation of an improved harmony. The hybrid system achieved throughput and residual energy values approximately 83.21% and 29.14% higher, respectively, than those of the component algorithms. The hybrid nature of HAS-PSO ensured a balance between exploitation and exploration capabilities, thereby contributing to energy efficiency. The system was suited for ensuring balanced energy efficiency for a prolonged network lifetime. Analysis showed that the hybrid HAS-PSO achieved better network lifetime, standard deviation, FND, and LND than LEACH, HSA, and PSO. * HABC-MBOA: Hybrid Artificial Bee Colony and Monarchy Butterfly Optimization Algorithm The HABC-MBOA was presented in Ref. [85] as a CH selection method based on ABC and ACO for effective clustering and improvement of network lifespan. The system was proposed to improve the balance between exploitation and exploration and prevent local search entrapment. The approach replaces the employee bee stage of ABC with the mutated butterfly adjusting operator of MBOA to avoid local minima entrapment. This new method addressed the issue of insufficiency in global search in the ABC method. The system achieved better throughput and residual energy values than some existing protocols, such as EPSOCHSS and FC-ABCAICHSS. * iCSHS: Integrated clustering and routing protocol for WSN using Cuckoo and Harmony Search This algorithm was proposed in Ref. [86].