Parameter Tuning for S-ABCPK: An Improved Service Composition Algorithm Considering Priori Knowledge

Ruilin Liu, Zhongjie Wang and Xiaofei Xu
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Ruilin Liu: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Zhongjie Wang: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China
Xiaofei Xu: School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China

International Journal of Web Services Research (IJWSR), 2019, vol. 16, issue 2, 88-109

Abstract: QoS-aware service composition problem has been drawn great attention in recent years. As an NP-hard problem, high time complexity is inevitable if global optimization algorithms (such as integer programming) are adopted. Researchers applied various evolutionary algorithms to decrease the time complexity by looking for a near-optimum solution. However, each evolutionary algorithm has two or more parameters, the values of which are to be assigned by algorithm designers and likely have impacts on the optimization results (primarily time complexity and optimality). The authors' experiments show that there are some dependencies between the features of a service composition problem, the values of an evolutionary algorithm's parameters, and the optimization results. In this article, the authors propose an improved algorithm called Service-Oriented Artificial Bee Colony algorithm considering Priori Knowledge (S-ABCPK) to solve service composition problem and focus on the S-ABCPK's parameter turning issue. The objective is to identify the potential dependency for designers of a service composition algorithm easily setting up the values of S-ABCPK parameters to obtain a preferable composition solution without many times of tedious attempts. Eight features of the service composition problem and the priori knowledge, five S-ABCPK parameters and two metrics of the final solution are identified. Based on a large volume of experiment data, S-ABCPK parameter tuning for a given service composition problem is conducted using C4.5 algorithm and the dependency between problem features and S-ABCPK parameters are established using the neural network method. An experiment on a validation dataset shows the feasibility of the approach.

Date: 2019
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