Improved Localization Algorithm Using Hybrid Firefly Genetic Algorithm in Wireless Sensor Network

This research work analysed the hybrid approach of Hybrid Genetic Algorithm -Firefly localization algorithm. The control variables, total number of sensor nodes, position known anchor nodes or landmark nodes, and the Genetic Algorithm-firefly localization problem are used to refine the Genetic Algorithm -firefly localization problem. The full number of iterations is taken into account, as well as the solution space. The distance between the sensor node and the anchor nodes is measured, and then the sensor node's location is estimated. Encoding, describe fitness feature, range, crossover, mutation, fitness function, beauty, distance, action, and stopping criteria are all part of the Hybrid Genetic Algorithm -Firefly localization algorithm implementation procedure are explained detailed. As the results it is demonstrated that the Hybrid Genetic Algorithm-Firefly Localization Algorithm iterated for 100 iterations has sensor node position knowledge estimation accuracy of 100% for firefly population vectors of 15, 18, and 20 with better time complexity.