QoS-aware web service composition using Gray Wolf Optimizer

In a service-oriented application, an integrated model of web services is composed of multiple abstract tasks. Each abstract task denotes a certain functionality that could be executed by a number of candidate web services with different qualities. The selection of a web service among candidates for execution of each task that is led to an optimal composition of selected web services is a NP-hard problem. In this paper, we adapt the Gray Wolf Optimizer (GWO) algorithm for selection of candidate web services whose composition is optimal. To evaluate the effectiveness of the proposed method, four quality parameters, response time, reliability, availability, and cost of web services are considered and the derived results are compared with several Particle Swarm Optimization (PSO) methods. The proposed method was executed in from 100 to 1000 times and the results showed that a better optimal rate (between 0.2 and 0.4) compared with PSO.