A Web Service Composition Algorithm Based on Global QoS Optimizing with MOCACO

Web services composition has gained a considerable momentum as a means to create and streamline B2B collaborations within and across organizational boundaries. This paper focuses on the web services composition and provides a novel selection algorithm based on global QoS optimizing and Multi-objective Chaos Ant Colony Optimization (MOCACO). Firstly, the web services selection model with QoS global optimization is converted into a multi-objective optimization problem. Furthermore, the MOCACO is used to select the service and optimize QoS to satisfy the user constraints. During the optimizing procedure, the random and ergodic chaos variable is used to make an optimal search, it overcomes the problem of low efficiency and easily being in a partial optimization that ant colony algorithm brings. The simulation shows that the MOCACO is more efficient and effective than Multi-objective Genetic Algorithm (MOGA) applied to services composition.