An approach for service composition optimisation considering service correlation via a parallel max–min ant system based on the case library. Issue 12 (2nd December 2018)

• Record Type: Journal Article
• Authors: Jian, Liu; Youling, Chen; Long, Wang; Lidan, Zuo; Yufei, Niu
• Is Part Of: International journal of computer integrated manufacturing. Volume 31:Issue 12(2018)
• Page Start: 1174
• Page End: 1188
• Publication Date: 2018-12-02
• Subjects: cloud manufacturing (CMfg) -- service composition optimisation (SCO) -- QoS correlation model -- parallel max–min ant system -- case library; Computer integrated manufacturing systems -- Periodicals; 670.427
• DOI: 10.1080/0951192X.2018.1529435
• Languages: English
• ISSNs: 0951-192X

Abstract

ABSTRACT: With the rapid development of cloud manufacturing, service composition optimisation (SCO) has become an important topic recently. Since the quality of service (QoS) varies widely in different service compositions due to the problem of service correlation, many SCO-based optimisation algorithms have been recently proposed to obtain a better service composition with an optimal QoS by combining it with the correlation-aware model. However, most existing approaches either consider the service correlation problem inadequately or suffer from a low efficiency of the optimisation algorithm. To address this problem, a novel optimisation algorithm named the parallel max–min ant system based on the case library (PMMAS-CL) is proposed, in which a comprehensive QoS correlation model is introduced with full consideration of the service correlation. In the PMMAS-CL algorithm, another special ant is employed to maintain the diversity of the population, and then a local learning strategy is adopted simultaneously to accelerate the convergence rate. Moreover, the case library, enhanced with an autonomous learning mechanism, is also applied to further improve the searching efficiency for the SCO problem. The experimental results show that the model significantly outperforms the previous approaches, and the PMMAS-CL algorithm can find the global optimal solution effectively compared with other state-of-the-art approaches.