Measuring service quality in service-oriented architectures using a hybrid particle swarm optimization algorithm and artificial neural network (PSO-ANN)

Abstract

Web service combination is an important task performed in different phases of the service-oriented architecture lifecycle. Measuring service quality based on the non-functional characteristics is an exceedingly difficult task. Therefore, this paper presents a Multilayer Perceptron Artificial Neural Network (MLPANN) to provide a method for measuring quality of service in a service-oriented architecture. To improve network performance, Particle Swarm Optimization (PSO) is used to optimize the weights of the network. Finally, our results are compared to those of a combination of Different Evolution (DE) algorithm and MLPANN in terms of Mean Square Error (MSE), Root Mean Square Error (RMSE) and Standard Deviation (STD). The results demonstrate the superiority of the proposed method.