Big data is one such technology. When we receive huge volume of data, there will be high demand in processing the huge data. It can also be said challenging task in big data processing. The increases in IoT devices in the network system collect more data to be processed in centralized devices called cloud storage. Every big data is processed and stored in the cloud. To overcome the performance and latency issues in large data computation, big cloud processing system uses edge computing in it. One of the key components of IoT is edge computing. We combine big data with cloud and edge computing in this paper as hybrid edge computing system. In the edge computing system, huge number of IoT devices computes services in its nearby network edge. Data sharing and transmission between the various service components may affect performance of the system. The main aim of this research article is to reduce the delay in data transfer between the components. This optimization goal is achieved by new Hybrid Meta-heuristic optimization (HMeO) algorithm. New HMeO algorithm designed for IoT devices to deploy the service components. MHO model is design to optimize the process by selecting the edge computing with minimum latency. Our proposed HMeO algorithm is compared with existing genetic algorithm and ant colony algorithm. The result shows HMeO algorithm provides more performance and efficient in in-depth data analysing and locating the component in big databased cloud environment.