Merged glowworm swarm with ant colony optimization for energy efficient clustering and routing in Wireless Sensor Network

Nowadays, in Wireless Sensor Network (WSN), the ability to transfer data over the network via a better route seems to be the tactic aspect due to certain criteria like network lifetime, energy consumption, and so on. A lot of efforts has been taken so far on better routing in the network via the clustering technique. Since clustering is an effective and apt way of providing a better route that transmits data without any conflicts. However, in the concept of clustering, the selection of Cluster Head (CH) is considered as a complex process as it has to satisfy certain parameters for effectual performance. If proper clustering is not made, the network will be suffered from network failures and energy depletion as well. To cope with these issues, this paper intends to find the optimal cluster head for energy-efficient routing protocol in WSN. As the main contribution deals with the Cluster Head Selection (CHS), this paper intends to propose a new hybrid algorithm namely Ant Colony Optimization (ACO) integrated Glowworm Swarm Optimization (GSO) approach (ACI-GSO), which is the hybridization of (GSO) and (ACO) algorithms. The objective of the CHS is to reduce the distance among the selected CH node. It makes the fitness function using multiple objectives like distance, delay, and energy. Finally, the performance of the proposed work is evaluated and the efficiency of the proposed work is proved over other conventional works.