Service Composition Optimization Using Differential Evolution and Opposition-based Learning

The numbers of web services are increasing rapidly over the last decades. One of the most interesting challenges in using web services is the usage of service composition that allows users to select and invoke composite services. In addition, the characteristic of each service is distinguished based on the quality of service (QoS). QoS is utilized in optimizing decisive factors such as cost or response time that is required by the user in the runtime system. Thus, QoS and service composition problem can be modeled as an optimization problem. In this study, differential evolution and opposition-based learning optimization methods have been proposed to obtain the optimal solution from candidate services. The results show that the proposed method converges faster than others. Therefore, the method is capable to select better composite services in short time.