A Method to Simplify Patterns in Web services Composition and Select Optimal Composition with a Probability Structure

One of the most challenging issues with web services is the composition of them, which is presented as a graph to show the interaction between services. Each node in this graph is called an abstract web service which their function is specified but the quality features are unclear. For each abstract service, there is a set of candidate services with the same function but different qualitative features. Replacing a candidate web service for each abstract service so that an optimal combination is achieved is an NP-hard problem that cannot be solved in polynomials, hence to solve it using heuristic algorithms. Several methods have been proposed for the web services optimal composition, but most of these methods don't support the probability structure. Only one method supports a probability structure that is not scalable for large graphs, constraint based, and analyzes each path of the graph separately. This paper presents an integrated scalable multi-objective approach for analyzing graph to not only covering two new patterns of nested loops and parallel loops, but also improving performance with representing a method for simplifying web-service composition. In this method, evolutionary algorithms are used for optimal web services selection and scalability. The two selected evolutionary algorithms are NSGAII and SPEAII. In the proposed method, first in conditional graphs, each path is repeated according to its probability, and then the NSGAII algorithm is used to determine the best path in the graph and find better solutions.  At the end of the article, the results of 8 methods are presented, which compared to the best of them, the proposed method has improved 30% in the reliability parameter and 121 milliseconds in response time.