Particle swarm optimization service composition algorithm based on prior knowledge

In order to quickly find an appropriate composition of services that meet the individual user’s requirements in the Internet big data, this paper proposes an improved particle swarm service composition method based on prior knowledge. This method firstly mines the service composition partial segments with certain frequencies of usage from a large number of historical service composition solutions, i.e. the service pattern. While receiving the user’s service composition requirement, this method uses the service pattern matching algorithm proposed in this paper to match the corresponding service patterns as a partial solution of this composition requirement. Then the method proposes an improved particle swarm algorithm for the part that do not successfully match the corresponding service patterns. This improved particle swarm algorithm has a mechanism to escape from the local optima. Finally, the method integrates the partial solutions of the two aspects into a complete solution, i.e. a complete service composition solution. This paper compares the optimality, time complexity and convergence with other related service composition optimization algorithms through simulation experiments. According to the analysis of the experimental results, the method proposed in this paper shows good performance in three aspects: optimality, time complexity and convergence.