A hybrid particle swarm optimization for service identification from business process

Service identification - as the first step of Service-Oriented Architecture -holds the main emphasis on the modeling process and has a broad influence on the system development. Selecting appropriate service identification method is essential for the prosperity of any service-oriented architecture project. Existing methods for service identification ignore the automation capability while providing human based prescriptive guidelines, which mostly are not applicable at enterprise scales. In this paper, we propose a top down approach to identify automatically services from business process. We use for clustering a hybrid particle swarm optimization algorithm and several design metrics for produce reusable services with proper granularity and acceptable level of cohesion and coupling. The experimental results show that our method HPSOSI (Hybrid Particle Swarm Algorithm for Service Identification) can achieve a high performance in terms of execution time and convergence speed.