Research on web service composition selection based on QoS metrics

Aiming at the hot and difficult issues in Web services and service composition, this paper analyzes and studies the selection of Web services and service composition from the functional and non functional attributes of Web services, and mainly makes the following work and innovation. First of all, the functional attributes of Web services are studied, Web services on various service provision platforms are collected, and Web services are classified. Secondly, aiming at the shortcomings of existing Web service QoS attribute evaluation models, an improved method is proposed, which introduces a comprehensive evaluation mechanism of variable weight vector. Based on the constant weight comprehensive evaluation method, the state variable weight vector is established to dynamically adjust the attribute weights of various service QoS indicators, so as to improve the accuracy and objectivity of the Web service QoS attribute evaluation. Finally, in view of the defects and deficiencies of traditional algorithms in the selection of Web service composition, the particle swarm optimization algorithm with linearly decreasing inertia weight and learning factor is introduced, which better balances the self cognition and social learning ability of particles, and improves the search speed and global search ability of particles. And a large number of experiments are carried out to compare it with the traditional algorithm, which verifies the effectiveness and superiority of the improved particle swarm optimization algorithm.