A New Hybrid Approach Using Genetic Algorithm and Q-learning for QoS-aware Web Service Composition

Abstract

Web Service composition (WSC) is a technology for building an application in Service Oriented Architecture (SOA). In WSC the sets of atomic Web services combine together to satisfy users’ requirements. Due to the increase in number of Web services with the same functionality and variety of Quality of Services (QoS), it became difficult to find a suitable Web service that satisfies the functional requirements, as well as optimizing the QoS. This has led to the emergence of QoS-aware WSC. However, to find an optimal solution in QoS-aware WSC is an NP-hard problem. In this paper, we propose a new approach that combines the use of Genetic Algorithm (GA) and Q-learning to find the optimal WSC. The performance of GAs depends on the initial population, so the Q-learning is utilized to generate the initial population to enhance the effectiveness of GA. We implemented our approach over the .NET Framework platform 4.7 using C# programming language. The experiment results show the effectiveness of our proposed approach compared to Q-learning algorithm and GA.