Abstract-This paper presents a solution to performance issues in the quality of service aware selection of Web services using techniques of parallelism and mechanisms of inference provided by Semantic Web. The results point to a significant improvement in the speed of searching Web services and thus makes the use of semantic resources viable in distributed systems to provide better quality of service to the clients.
This paper presents a new parallel hybrid algorithm combining Particle Swarm Optimization (PSO), Differential Evolution (ED) and Genetic Algorithms (GA) for optimizing unconstrained numerical functions. Basically, PSO and ED evolves independently in our proposal, then they cooperate between them exchanging their best individual, which undergo GA operators locally in order to search different areas in the search space. The results are evaluated concerning the quality of the solution and speedup against six benchmarks functions. The outcomes are compared with GA, PSO and ED in the serial version and with the classical island model of PSO and DE, considering two and four threads. A comparison with a hybrid algorithm is done as well.
Abstract-This paper proposes a solution to performance issues in the selection of Web services based on Quality of Service (QoS) that uses inference mechanisms from Semantic Web resources. Although several researchers highlight the benefits provided by the use of the Semantic Web techniques for searching and compose Web services with QoS, it can become costly when it depends on the approach adopted. Thus, we present a web service selection that uses QoS information in a replicated and distributed ontologies over different service providers. The results point to a significant improvement in the ontology inference process and thus makes the use of semantic resources viable in distributed systems to provide better QoS.
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