Throughput Comparison using Artificial Bee Colony (ABC) Algorithm with Dynamic Technique for Improving Data Optimization

The artificial bee colony algorithm may be an effective optimization method for the acquisition model of bees where Clustering is a good approach to provide a better route that doesn't cause any problems when transmitting data. Also, clusters have a high degree of resemblance within themselves but a low degree of similarity between them. For processing large dimensional data, the usual optimization approach is ineffective. Hence, this paper introduces Throughput Comparison using Artificial Bee Colony (ABC) Algorithm with Dynamic Technique for Improving Data Optimization Technique to generate an initial population of pathways connecting the source and destination node. Consequently, to pick a food source Employee bees linked with explicit food sources, onlooker bees watching the movement of employee bees inside the hive to pick a food source, and scout bees looking for food sources randomly make up the condition of artificial bees in the ABC algorithm. This paper show the better throughput as compare to FANET-GSO, IGSO, UCRA-GSO and ACI-GSO Techniques.