Affiliations: [a] Department of Computer Science, Faculty of Sciences, Sétif 1 University, Sétif, Algeria
| [b] Intelligent Systems Laboratory, Faculty of Technology, Sétif 1 University, Sétif, Algeria
Correspondence:
[*]
Corresponding author. Fateh Seghir, Intelligent Systems Laboratory, Faculty of Technology, Sétif 1 University, Sétif, Algeria. E-mail: [email protected].
Abstract: In this paper, we introduce an extended version of artificial bee colony with a local search method (EABC) for solving the QoS uncertainty-aware web service composition (IQSC) problem, where the ambiguity of the QoS properties are represented using the interval-number model. At first, we formulate the addressed problem as an interval constrained single-objective optimization model. Then, we use the skyline operator to prune the redundant and dominated web services from their sets of functionally equivalent ones. Whereas, EABC is employed to solve the IQSC problem in a reduced search space more effectively and more efficiently. For the purpose of validation of the performance and the efficiency of the proposed approach, we present the experimental comparisons to an existing skyline-based PSO, an efficient discrete gbest-guided artificial bee colony and a recently provided Harris Hawks optimization with an elite evolutionary strategy algorithms on an interval extended version of the public QWS dataset.
Keywords: Web service composition, Quality of Service (QoS), interval number, skyline operator, artificial bee colony
