Web service selection with global constraints using modified gray wolf optimizer

Web service composition is the process of assembling new services from the existing individual services. Due to the plenty of web services exhibiting similar functionality but different in terms of Quality-of-Service (QoS) requirements, the selection of an optimal service is a very crucial task. Consequently, the composition of web services with optimal QoS has become an important as well as the challenging problem. In this paper, a population-based optimization technique namely modified gray wolf optimizer (MGWO) is used for selecting the optimal set of services. We test the MGWO over the test case from the public repository of 2500 web services with nine different QoS attribute. The experimental results divulge that: (a) the MGWO is an effective and powerful approach to extract optimal services in diverse situations, and (b) the MGWO performs better than two other state-of-the-art algorithms: GA and GWO, finding the services with optimized QoS.