Paper Info
Title
Parameter Tuning For S-Abcpk An Improved Service Composition Algorithm Considering Priori Knowledge
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-ABC(PK)) to solve service composition problem and focus on the S-ABC(PK)'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-ABC(PK) 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-ABC(PK) parameters and two metrics of the final solution are identified. Based on a large volume of experiment data, S-ABC(PK) parameter tuning for a given service composition problem is conducted using C4.5 algorithm and the dependency between problem features and S-ABC(PK) parameters are established using the neural network method. An experiment on a validation dataset shows the feasibility of the approach.
Year
DOI
Venue
2019
10.4018/IJWSR.2019040105
INTERNATIONAL JOURNAL OF WEB SERVICES RESEARCH
Keywords
Field
DocType
Artificial Bee Colony (ABC) Algorithm, Parameter Tuning, Priori Knowledge, QoS-Aware Service Composition
Data mining,Computer science,Service composition
Journal
Volume
Issue
ISSN
16
2
1545-7362
Citations 
PageRank 
References 
0
0.34
0
Authors
3
Name
Order
Citations
PageRank
Ruilin Liu115917.17
Zhong-Jie Wang235664.60
Xiaofei Xu340870.26