# 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  
The algorithms in this group are inspired by the natural life patterns of several animals or insects. Some of the algorithms in this group include Artificial Bee Colony (ABC), Grasshopper Optimization, Firefly Algorithm, Bacterial Foraging Optimization, and Particle Swarm Optimization (PSO). The swarm intelligence category as an optimization method shares two similar attributes with evolutionary computation techniques (i.e., population-based design and stochastic search). The following sub-section introduces swarm intelligence and provides the necessary background for understanding its basic concepts. The design of unorthodox optimization techniques in evolutionary computation is based on modeling or incorporating concepts and theories extracted from sociology, natural science, or psychology. An evolutionary algorithm, as earlier stated, mimics the concept of evolution by imitating the theories of genetics and natural selection in order to find the optimal solutions to optimization problems. Swarm Intelligence-based approaches are discussed here and compared by employing performance and features of optimization parameters to evaluate their working efficiency. These parameters aim to provide a general overview and comparison of these protocols. The features-based comparison is given in Table 4.  
* ABC-SD: An energy-efficient cluster-based routing algorithm  
The ABC-SD for cluster-based routing protocols was introduced in Ref. [5] for energy efficiency in WSNs; this new approach achieved low power consumption by exploiting ABC's search features (foraging behavior). The centralized control mechanism was also used to implement an LP formulation that relied on a multi-objective fitness function within the BS. The clusters were built by exploiting the efficient features of ABC, while communication between clusters was achieved based on the choice of CHs during clustering. The cost-based measure for optimal routing path selection was based on the energy-hop count balance; hence, it is a distributed approach. Comparison of the ABC-SD technique with other methods (such as PSO-C, LEACH-C, LEACH, ABC-C) in different network sizes showed that the protocol achieved better efficiency in coverage, network lifetime, and packet delivery ratio. * ICWAQ: Improved version of cluster based WSN ABC Quality  
Ref. [76]'s proposed algorithm, ICWAQ, is a system that relies on a message transferring technique to determine the distance between sensors in a WSN. This information is sent to the BS for the selection of CHs using an ABC algorithm. The choice of CHs is communicated to the network for the other nodes to find their closest CH based on signal strength. ICWAQ was developed with the aim of reducing WSN energy consumption. The performance of the proposed system was evaluated in comparison with LEACH and found to be up to 70% more energy efficient when the position of the BS was close to the network edge. However, ICWAQ introduced a significant level of overhead in the set-up phase because of the huge volume of messages passing between the sensors.