Web service selection using particle swarm optimization and genetic algorithms

Current service-oriented architecture standards mainly rely on functional properties, however, service registries lack mechanisms for managing services´ non-functional properties. These non-functional properties are expressed as quality of service (QoS) attributes. The user can describe the request of a service in terms of QoS attributes, i.e., the user aims for good service performance, e.g. low waiting time, high reliability and availability. This paper investigates service selection, and proposes two approaches; one which is based on a genetic algorithm and the other is based on a particle swarm optimization approach to match consumers with services based on QoS attributes as closely as possible. Both approaches are compared with an optimal assignment algorithm called the Munkres algorithm. Measurements are performed to quantify the overall match score, the execution time, and the scalability.