Medical Services Optimization Using Differential Evolution

This paper proposes a method to compose and optimize medical services as business workflows. Such a workflow consists in a set of abstract services, and for each abstract service there are several concrete services. Since each medical service has different QoS (Quality of Service) parameters such as

response time

,

rating

,

distance

and

cost

, determining the optimal combination of concrete services that realize the abstract services of the business workflow is an NP hard problem. Recent proposals for solving NP optimization problems indicate the Genetic Algorithms (GA) as the best approach for complex workflows. But this problem usually needs to be solved at runtime, a task for which GA may be too slow. We propose a new approach, based on Differential Evolution (DE), that converges faster and it is more scalable and robust than the existing solutions based on Genetic Algorithms.