Intelligent PSO approach for QoS-Aware Web Service Selection

Various providers can supply the various services, in a service-oriented setting, each with its own unique set of non-functional Quality of Service (QoS) qualities. Web service selection efficiency will always decay as the number of offered services grows because many already-existing services share identical QoS characteristics. There must be a method for choosing which individual services will go into other related fields so that it may fulfill its consumers' expectations for quality of service. Many services exist with the same QoS and provide the same functionality therefore choosing the best services from many candidates is a research issue. When selecting services of good quality and with less computation time, it will affect the efficiency of the next operations such as service composition and service recommendation. In this paper, a new improved Particle Swarm Optimization (PSO) algorithm is proposed to solve the mentioned issue of service selection. To find the global optimal solution for selecting dynamic web services while maintaining the quality of service, this study introduces a new single parameter and evolution-based enhancement system to improve PSO-based web service selection (IPSO-SS). The IPSO tries to avoid the local optimum to find the global solution for the problem of service composition. IPSO-SS is practical and effective in the service selection process, as evidenced by theoretical experimental and analysis findings. The experiments conducted on QWS datasets showed that the proposed approach IPSO-SS outperformed other approaches in terms of computation time and convergence rate.