Using an Estimation of Distribution Algorithm to Achieve Multitasking Semantic Web Service Composition

被引:7
作者
Wang, Chen [1 ]
Ma, Hui [2 ]
Chen, Gang [2 ]
Hartmann, Sven [3 ]
机构
[1] Natl Inst Water & Atmospher Res, HPC & Data Sci Dept, Wellington 6021, New Zealand
[2] Victoria Univ Wellington, Sch Engn & Comp Sci, Wellington 6041, New Zealand
[3] Tech Univ Clausthal, Dept Informat, D-38678 Clausthal Zellerfeld, Germany
关键词
Semantic Web; Learning systems; Web services; Heuristic algorithms; Semantics; Estimation; Quality of service; Combinatorial optimization; estimation of distribution algorithm (EDA); evolutionary multitasking; quality of services (QoS) optimization; Web service composition;
D O I
10.1109/TEVC.2022.3170899
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Web service composition composes existing Web services to accommodate users' requests for required functionalities with the best possible quality of services (QoS). Due to the computational complexity of this problem, evolutionary computation (EC) techniques have been employed to efficiently find composite services with near-optimal functional quality (i.e., quality of semantic matchmaking, QoSM for short) or nonfunctional quality (i.e., QoS) for each composition request individually. With a rapid increase in composition requests from a growing number of users, solving one composition request at a time can hardly meet the efficiency target anymore. Driven by the idea that the solutions obtained from solving one request can be highly useful for tackling other related requests, multitasking service composition approaches have been proposed to efficiently deal with multiple composition requests concurrently. However, existing attempts have not been effective in learning and sharing knowledge among solutions for multiple requests. In this article, we model the problem of collectively handling multiple service composition requests as a new multitasking service composition problem and propose a new permutation-based multifactorial evolutionary algorithm based on an estimation of distribution algorithm (EDA), named PMFEA-EDA, to effectively and efficiently solve this problem. In particular, we introduce a novel method for effective knowledge sharing across different service composition requests. For that, we develop a new sampling mechanism to increase the chance of identifying high-quality service compositions in both the single-tasking and multitasking contexts. Our experiment shows that our proposed approach, PMFEA-EDA, takes much less time than existing approaches that process each service request separately, and also outperforms them in terms of both QoSM and QoS.
引用
收藏
页码:490 / 504
页数:15
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