Authors: Yiwen Zhang; Guangming Cui; Shu Zhao; Jie Tang
Addresses: Department of Computer Science and Technology, Anhui University, Hefei, Anhui, China ' Department of Computer Science and Technology, Anhui University, Hefei, Anhui, China ' Department of Computer Science and Technology, Anhui University, Hefei, Anhui, China ' Department of Computer Science and Technology, Tsinghua University, Beijing, China
Abstract: In the service-oriented environment, large-scale Service Composition (SC) has become an important research, where Quality of Service (QoS) has been applied widely. QoS is generally used to represent non-functional properties of web services and differentiate them with the same functionality. Studying how to select appropriate services for a composition from a very large number of similar candidate services is an NP-hard problem. However, there are limitations among existing methods, e.g. poor scalability and massive calculation in the exhaustion method, slow convergence speed and easy to fall into local optimum in the traditional evolutionary computation method. Thus, in this paper, an improved fruit fly optimisation algorithm called IFOA4WSC is proposed, which is quick and effective for web service composition. We analyse the convergence of fruit fly swarms in IFOA4WSC algorithm, and carry out a large number of simulation experiments in the common and random data sets. Experimental results show that IFOA4WSC is effective and highly efficient, and has better stability against classical PSO, GA and FOA.
Keywords: IFOA4WSC; web service composition; QoS; quality of service; convergence analysis; web services; fruit fly optimisation; simulation; swarm intelligence.
International Journal of Web and Grid Services, 2016 Vol.12 No.1, pp.81 - 108
Received: 04 Jul 2015
Accepted: 13 Aug 2015
Published online: 14 Jan 2016 *