# Optimization of Clustering in Wireless Sensor Networks: Techniques and Protocols
## 6 Recent Advancements in Clustering Optimization Algorithms
### Meta-Heuristic
#### 6.1.1 Evolutionary Algorithms  
These algorithms are used for routing and clustering schemes; the Genetic Algorithm (GA) is the commonly used evolutionary algorithm for routing and clustering in WSNs. The GA is used to prolong the lifetime of CHs in order to improve network life [95] and network efficiency [96]. Different studies have presented hybrids of GA and Artificial Bee Colony (ABC) methods to enhance the clustering process, improve optimal routing through nodes, and improve the QoS [97; 98]. This combination also reduced data energy usage in each round by reducing the total distance [99]. Despite the ability of the GA to solve multi-dimensional, non-continuous, non-parametrical, and non-differential problems, it still suffers from prolonged execution times, and cannot produce steady optimization response times in large populations. The protocols of clustering evolutionary algorithms-based approaches have been investigated using different parameters. to provide a general overview of the characteristics of their optimized Algorithms in clustering, Table 3 summarizes these studies based on their optimization methodology features. * GATER: GA-based threshold sensitive energy-efficient routing protocol  
The GATERP proposed in Ref. [70] performed CH selection using GA parameters such as cohesion and cluster division. The system was equipped with an inter-cluster data transmission algorithm to elongate the network lifetime. Load balancing was improved by introducing a GA-based multi-hop communication mechanism that minimized energy usage. The GA was used to establish communication between the BS and the cluster heads; this aimed to achieve an optimal link cost for load balancing between distant CHs and reducing energy usage. The performance of the system was evaluated in terms of energy efficiency and energy utilization. * HACH: Heuristic algorithm for clustering hierarchy protocol  
The authors of Ref. [71] introduced the HACH protocol, which was developed for the selection of CHs and non-active nodes in each round using a stochastic sleep scheduling algorithm; this allowed SNs to enter a sleep state without disturbing the network performance. Energy usage and CH distribution in the WSN were also regulated by introducing a novel heuristic crossover operator. This system improved network performance by extending its lifespan under different energy heterogeneity settings. * MRP-ACO: A multipath routing protocol  
The multipath routing protocol (MRP) was developed [72] based on a combination of dynamic clustering and Ant Colony Optimization (ACO). The three phases of the algorithm are as follows: the initial phase is the selection of CHs based on their signal strength and residual energy; this step is aimed at prolonging the network lifespan. The second phase uses the ACO to establish the multiple paths between CHs and SNs. The multiple paths were established with minimal energy cost using an improved ACO algorithm. The final phase is the selection by the CHs of dynamic routes for data transmission. In this algorithm, three types of ants were used: search ants (SANTs), backward ants, and abnormal ants. A new CH was selected whenever the current CH had residual energy of <50% of the average energy of the remaining nodes in that cluster with a stronger RSSI. The selected CH would initiate a new path discovery process when there were less than two multiple paths, implying a significant decline in the path reliability.