A Hybrid Glowworm Swarm Optimization Using in Smart Sensor Network for Electric Grids

Abstract

The special features of smart grid technology includes regular metering communication, renewable power incorporation, allotment computerization and whole scrutinizing and organization of complete power grid. Little micro-electrical automatic schemes that are deployed in collecting and broadcasting data from ambience are Wireless Sensor Networks (WSNs). Providing security and energy consumptions are the most emerging issues in Wireless sensor network communication. To enhance the lifespan of a network, energy efficiency should be increased by decreasing energy consumption of the sensor nodes, thus striking a balance in the power consumption of each node. As the primary source of origin of energy consumption of sensor nodes is long distance transmission of data, good impact on energy consumption can be provided through an efficient routing protocol. So as to enhance the lifespan of a network, a number of protocols have been put forth in the form of optimization algorithms. This study involves Glowworm Swarm Optimization (GSO). In GSO, a probabilistic cost is computed by every glowworm in spite of finding its neighboring glowworm that has the enhanced luciferin intensity than others. Based on this probability cost, a glow worm moves towards the chosen glowworm. To enhance GSO performance, the GSO hybrid optimized with Harmony Search (HS) and Tabu Search (TS) local methods are also proposed. Results prove that proposed method achieves better performance.