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DE-caABC: differential evolution enhanced context-aware artificial bee colony algorithm for service composition and optimal selection in cloud manufacturing

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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.

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  1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, Guangdong, 510640, China

    Jiajun Zhou & Xifan Yao

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  1. Jiajun Zhou
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  2. Xifan Yao
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Correspondence to Xifan Yao.

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Zhou, J., Yao, X. DE-caABC: differential evolution enhanced context-aware artificial bee colony algorithm for service composition and optimal selection in cloud manufacturing. Int J Adv Manuf Technol 90, 1085–1103 (2017). https://doi.org/10.1007/s00170-016-9455-x

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