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Issue title: Applications of intelligent & fuzzy theory in engineering technologies and applied science
Guest editors: Álvaro Rocha
Article type: Research Article
Authors: Li, Jinga; * | Yuan, She Fengb
Affiliations: [a] College of Computer Science and Technology, ZhouKou Normal University, Henan Province, China | [b] College of Information Engineering, Henan Vocational College of Agriculture, Henan Province, China
Correspondence: [*] Corresponding author. Jing Li, College of Computer Science and Technology, ZhouKou Normal University, Wenchang Avenue, Chuanhui District of Zhoukou City, Henan Province 466001, China. Tel.: +86 15994175799; E-mail: zknulj@163.com.
Abstract: Logistics Web Service optimal composition is the key business of the fourth party logistics service platform and how to construct the optimal logistics Web service composition is a challenge. However, existing logistics Web service composition methods only consider the general quality of service (QoS) and ignore the domain quality attribute of the logistics service, leading to unsatisfactory composite logistics Web services and poor success rate of logistics composition. For solving this problem, a domain quality-driven logistics optimal logistics Web service composition method is proposed. Firstly, domain quality evaluation model of logistics Web service is proposed; secondly, quality evaluation model of logistics Web service composition has been designed in which domain quality is taken as the primary indicator and general QoS attribute is taken as the secondary index; Finally, the improved artificial bee colony algorithm is incorporated within the framework of the cultural algorithm to construct culture artificial bee colony algorithm(C-ABC), and this algorithm is applied to solve the problem of domain quality-driven logistics Web service optimal composition. Experimental results show that the method is effective and feasible.
Keywords: Domain quality, QoS, logistics web service composition, cultural algorithm, artificial bee colony algorithm
DOI: 10.3233/JIFS-169079
Journal: Journal of Intelligent & Fuzzy Systems, vol. 31, no. 4, pp. 2383-2391, 2016
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