Adaptive MOEA/D for QoS-Based Web Service Composition

Autor: Marcel Cremene, Mihai Alexandru Suciu, Dumitru Dumitrescu, Denis Pallez
Přispěvatelé: Laboratoire d'Informatique, Signaux, et Systèmes de Sophia-Antipolis (I3S) / Projet MinD, Scalable and Pervasive softwARe and Knowledge Systems (Laboratoire I3S - SPARKS), Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Université Nice Sophia Antipolis (... - 2019) (UNS), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA)-Laboratoire d'Informatique, Signaux, et Systèmes de Sophia Antipolis (I3S), COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-COMUE Université Côte d'Azur (2015-2019) (COMUE UCA)-Centre National de la Recherche Scientifique (CNRS)-Université Côte d'Azur (UCA), Computer Science Research Laboratory [Cluj-Napoca] (LCI), Babes-Bolayi Universtity, Middendorf, Martin and Blum, Christian
Rok vydání: 2013
Předmět:
Zdroj: Evolutionary Computation in Combinatorial Optimization ISBN: 9783642371974
EvoCOP
Evolutionary Computation in Combinatorial Optimization: 13th European Conference, EvoCOP 2013, Vienna, Austria, April 3-5, 2013. Proceedings
Middendorf, Martin and Blum, Christian. Evolutionary Computation in Combinatorial Optimization: 13th European Conference, EvoCOP 2013, Vienna, Austria, April 3-5, 2013. Proceedings, Adaptive MOEA/D for QoS-Based Web Service Composition, Springer Berlin Heidelberg, pp.73--84, 2013, 978-3-642-37198-1. ⟨10.1007/978-3-642-37198-1_7⟩
DOI: 10.1007/978-3-642-37198-1_7
Popis: QoS aware service composition is one of the main research problem related to Service Oriented Computing (SOC). A certain functionality may be offered by several services having different Quality of Service (QoS) attributes. Although the QoS optimization problem is multiobjective by its nature, most approaches are based on single-objective optimization. Compared to single-objective algorithms, multiobjective evolutionary algorithms have the main advantage that the user has the possibility to select a posteriori one of the Pareto optimal solutions. A major challenge that arises is the dynamic nature of the problem of composing web services. The algorithms performance is highly influenced by the parameter settings. Manual tuning of these parameters is not feasible. An evolutionary multiobjective algorithm based on decomposition for solving this problem is proposed. To address the dynamic nature of this problem we consider the hybridization between an adaptive heuristics and the multiobjective algorithm. The proposed approach outperforms state of the art algorithms.
Databáze: OpenAIRE