Title: QoS-aware web service selection based on self-organising migrating algorithm and fuzzy dominance

Authors: Amal Halfaoui; Fethallah Hadjila; Fedoua Didi

Addresses: Computer Science Department, LRIT Laboratory, Tlemcen University, BP 119-13000 Tlemcen, Algeria ' Computer Science Department, LRIT Laboratory, Tlemcen University, BP 119-13000 Tlemcen, Algeria ' Computer Science Department, LRIT Laboratory, Tlemcen University, BP 119-13000 Tlemcen, Algeria

Abstract: Web service composition consists of creating a new complex web service by combining existing ones. The selection of composite services is a very complex and challenging task, especially with the increasing number of services offering the same functionality. The web service selection can be considered as a combinatorial problem which focuses on delivering the optimal composition that satisfies the user's requirements (functional and non-functional needs). Several optimisation algorithms have been proposed in the literature to tackle the web service selection. In this work, we propose an approach that adapts a recent stochastic optimisation algorithm called self-organising migrating algorithm (SOMA) for QoS web service selection problem. Furthermore, we propose a fuzzification of the Pareto dominance and use it to improve SOMA by comparing the services within the local search. The proposed approach is applicable to any combinatorial workflow with parallel, choice and loop pattern. We test our algorithm with a set of synthetic datasets and compare it to the most recently used algorithm (PSO). The comparative study shows that SOMA produces promising results, and therefore, it is able to select the user's composition in an efficient manner.

Keywords: web service selection; self-organising migrating algorithm; SOMA; fuzzy dominance; swarm-based optimisation algorithms.

DOI: 10.1504/IJCSE.2018.096026

International Journal of Computational Science and Engineering, 2018 Vol.17 No.4, pp.377 - 389

Received: 09 Feb 2016
Accepted: 10 Jul 2016
Published online: 09 Nov 2018 *

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