AN OPTIMAL COMPOSITION PLAN SELECTION USING MULTI OBJECTIVE PARTICLE SWARM OPTIMIZATION

Domain-ontology based Particle Swarm Optimization (PSO)-inspired Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH) and Improved Bipartite graph is an efficient web service composition approach. It composed a number of web services to achieve high customer satisfaction. It also improves the clustering quality and reduced the processing time of web service composition. The most verification problems are easily verified by using Petri Net in planning, verification and execution phase. In order to provide more efficient automatic web service composition, in this paper multiple Quality of Service (QoS) parameters like cost, accuracy, accessibility, robustness, scalability, modifiability and security are included in Petri net-based algebra approach. While adding a greater number of QoS parameters there will be multi-objective services composition optimization problem. It is solved by introducing a Multi-Objective Genetic Algorithm (MOGA) and Multi-Objective PSO (MOPSO) algorithm. It selects qualitative different services from multiple functionally identical web services which achieve the best reliability model. Thus, the automatic web service composition is performed for discovering the appropriate services by satisfying the customer requirements. Experimental results show that the proposed method performs better than the existing method