A Global QoS Optimizing Web Services Selection Algorithm Based on MOACO for Dynamic Web Service Composition

Dynamic web service composition is a key technology for building the service-oriented, loose coupling and integrated applications. In a Web service composition system Web services can be selected and integrated to generate a workflow for the satisfaction of user functional requirements. Usually, for each task in the workflow a set of alternative Web services with similar functionality is available, and that these Web services have different QoS parameters. This leads to the general optimization problem of how to select Web Services for each task so that the overall QoS requirements of the composition are satisfied and optimal. However, the existing approaches are almost QoS local optimization or mono-objective based, and can not achieve global constraints set by user. In this paper, we present a novel global QoS optimizing and multi-objective Web services selection algorithm based on multi-objective ant colony optimization (MOACO) for the dynamic Web service composition. Firstly, we propose a model of Web services selection with QoS global optimization and convert this problem into a multi-objective optimization problem with user constraints. Furthermore, based on the MOACO, the proposed algorithm simultaneously optimizes the different QoS parameters of the workflow. In this way, a set of optimal solutions, known as Pareto set, is calculated. Experimental results prove the proposed algorithm outperforms the recently published QoS global optimization based on multi-objective genetic algorithm (MOGA), specially designed for solving the Web services selection problem.