Leveraging Genetic Algorithm to Compose Web Services in a Context-Aware Environment

In a context-aware pervasive environment, it is crucial to provide an efficient adaption mechanism to rapidly discover appropriate services and compose them to achieve user desired situation when context change. In this paper, we propose a GA(Genetic Algorithm) based novel service composition method in a context-aware environment. We first use Context Space model to represent context, situation, user preference and services in a unified formal way. Then we transform the service composition problem into a multi-objective optimization problem and utilize GA to find the optimal service composition based on user´s preference. To affiliate the connection between context and services, we use a vector to represent each service to reflect which context types a service can change so as to utilize GA to find the best service composition. Confliction of services may happen during the GA process. In order to resolve the conflicts, we propose a SST(Service Similarity Tree) method to measure the similarity among services to find out the best alternative. Finally, we design and implement a simulation experiment to verify our method. The results shows that our method can leverage GA to synthesis appropriate services to achieve user desired goal in an efficient way.