Genetic Algorithm for Context-Aware Service Composition Based on Context Space Model

Zhichao Zhang, Shaoqiu Zheng, Weiping Li, Ying Tan, Zhonghai Wu, Wei Tan

Abstract: The emergence of Web services has changed the Internet a lot, and greatly facilitated the development of service based software systems. How to select appropriate services and compose them according to given context to satisfy a user's requirement is a big challenge. This paper proposes a novel Genetic Algorithm (GA) method to synthesis web services in a context-aware environment. We first present a context space model to illustrate both contexts and services in a formal way, we utilize GA to compose context-aware services according to users' preference. We transform the problem of service composition to a multi-objective optimization problem. To resolve the conflict and dependencies among services in GA process, we propose a service similarity tree (SST) model to measure the similarity between services. Finally, we design a simulation experiment to evaluate our method. The experiment result shows that our method is a promising one to solve service composition problem in a context-aware environment.