Entropy Weight Method-Based Multi-Attribute Decision-Making for Web Service Selection

Web services continue to proliferate and users are faced with more choices. Learning to choose Web services wisely is essential in order to meet demand and receive the best service. This paper focuses on the problem of selecting the best Web service in a multi-attribute state. Our goal is to select quality service streams from a vast array of multi-attribute services to meet our users' needs. We consider the user's requirements as a workflow that includes a number of tasks and each task consisting of various services that are also part of the user's needs. It is our responsibility to pick the best services from each task and integrate them into a seamless service process. To solve this problem, in this work, we utilized a normalization algorithm for the attributes of the candidate services and then used entropy weighting method to calculate the weights and the optimal particle swarm algorithm for service flow selection. Although similar research methods exist in the existing literature, experimental results demonstrate that the time performance of this method in this paper can be significantly improved.