Semantics Web Service Characteristic Composition Approach Based on Particle Swarm Optimization

Service composition is one of the main behavior in the SOC(Service-Oriented Computing) process, which direct and indirect influences effectiveness and precision of service computing; But at present, relation researches mainly focus on semantics recognition and QoS(Quality of Service). In the paper, according to semantics characteristic classification, we proposed a semantics web service characteristic composition approach based on particle swarm optimization, and set up a characteristic selsection mechanism of semantics web service, and adopt charecteristic distance relation to implement service characteristic classification, and use the distance relation to build characteristic tendency degree, sufficiency and characteristic extractor computing formula of semantics web service, at the same time, according to the formula, to implement service characteristic composition algorithm. Then, we set up a optimal mathematical model via characteristic extractor formula. And employ particle swarm to optimize the model and Amazon service set to make experiment, which showed that it is feasible and effective.