A Multiobjective Discrete Grey Wolf Optimization Approach for Transactional and QoS-driven Web Services Composition
Sunita Jalal, Dharmendra Kumar Yadav · Applied Artificial Intelligence · 2021
Web services facilitate reusability that allows cost-effective development of business applications using web services composition. Due to the proliferation of web services, different service providers are providing similar functionality web services. But these web services can have different values for QoS attributes and transactional properties. Thus, it is difficult to build a transactional and QoS optimal composite web service. Most of the existing works used the scalarization-based method for selecting optimal composite web service. In the scalarization-based method, the service user should have a priori knowledge of its preferences about the nonfunctional requirements of desired solutions. This paper proposes a Multiobjective Discrete Grey Wolf Optimization (MDGWO)-based approach for Transactional and QoS-driven Web Services Composition. The Pareto dominance concept is used to select optimal composite web service. Generational Distance (GD), Inverse Generational Distance (IGD), and Spread measures are used to evaluate the performance of the proposed approach. Experimental results indicate that the proposed approach performs well.