The aerial vehicles are highly flexible and cost effective so that it is able to control the vehicles in a better manner. Currently the aerial vehicles are used to perform highly confidential data transmission in a collaborative way. Several challenges occurred in search works in terms of limited battery power and environmental condition. To overcome this in this article latency aware optimization for collaborative aerial vehicles (ELAOC-UAVs) are developed. The core modules of this process are traffic model and trajectory design creation and optimization among the unmanned aerial vehicles (UAVs) using glowworm swarm optimization (GSO) algorithm. With the presence of this process the delay occurrences among the aerial vehicles are greatly reduced and that helps to improve the overall performance of the network. The ELAOC-UAVs model is used to measure the performance of the network are data accuracy, data loss, routing overhead, network throughput and average delay. From the final result, it has been proven that the ELAOC-UAVs obtained better results in terms of throughput and data accuracy when compared with the earlier baseline methodology.
