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- Title
DE-caABC: differential evolution enhanced context-aware artificial bee colony algorithm for service composition and optimal selection in cloud manufacturing.
- Authors
Zhou, Jiajun; Yao, Xifan
- Abstract
Cloud manufacturing (CMfg) is a new type of service-oriented manufacturing paradigm, in which all kinds of manufacturing resources are encapsulated as manufacturing services (MSs) and can be invoked by customers on demand. Manufacturing service composition (MSC) is a key technology in CMfg for creating value-added services to complete complicated manufacturing tasks by aggregating qualified MSs together. However, current MSC approaches have some drawbacks and there still exist some issues remained to be solved: (1) large quantities of candidate services increase the complexity of service dynamic composition, which poses scalability concerns and on-demand efficient solutions; (2) the service domain features (e.g., service prior, correlation, and similarity) that have a strong influence on the efficiency of service composition are not considered adequately, which causes undesirable efficiency in practical service applications; and (3) dynamic characteristics of QoS (quality of service) values in an open network environment are not considered adequately. To effectively address such problems, this paper first proposes a context-aware artificial bee colony (caABC) algorithm based on the principle of ABC and service features in the cloud environment. Then the differential evolution-enhanced caABC, i.e., the so-called DE-caABC, is designed to increase the searching performance of ABC further. Additionally, dynamics of trust QoS is investigated with the introduction of time decay function. Finally, the feasibility and effectiveness of DE-caABC are validated through the experiments.
- Subjects
CLOUD computing; CONTEXT-aware computing; ADVANCED planning &; scheduling; BEES algorithm; QUALITY of service
- Publication
International Journal of Advanced Manufacturing Technology, 2017, Vol 90, Issue 1-4, p1085
- ISSN
0268-3768
- Publication type
Article
- DOI
10.1007/s00170-016-9455-x