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A many-objective memetic algorithm for correlation-aware service composition in cloud manufacturing

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  • Fei Wang
  • Yuanjun Laili
  • Lin Zhang

Abstract

Service composition is a core issue of cloud manufacturing (CMfg) to integrate distributed manufacturing services for customised manufacturing tasks. Existing studies focus on the quality of service (QoS) in composition by assuming that each service is independent with each other. However, the correlation between services determines whether a composition is feasible in practice and is a primary factor of its QoS. This paper considers two typical correlations, composability-oriented correlation and quality-oriented correlation. The composability-oriented correlation is modelled as a group of constraints to decide whether a solution is feasible. The influence of the quality-oriented correlation between two services on the overall QoS of a composition is quantified by a discount percentage based on their correlation degrees. A mathematical model of correlation-aware service composition is then established. To solve this problem, a many-objective memetic algorithm termed HypE-C (Hypervolume Estimation Algorithm for Multiobjective Optimisation involving Correlation) is designed. Three correlation-based local search strategies are established in the frame of HypE (Hypervolume Estimation Algorithm for Multiobjective Optimisation) to achieve better trade-off among multiple conflicting QoS criteria. Experiments demonstrate the effectiveness of the proposed algorithm HypE-C compared with five many-objective algorithms on eliminating infeasible search space and providing high QoS service composition solutions.

Suggested Citation

  • Fei Wang & Yuanjun Laili & Lin Zhang, 2021. "A many-objective memetic algorithm for correlation-aware service composition in cloud manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 59(17), pages 5179-5197, September.
    • Handle: RePEc:taf:tprsxx:v:59:y:2021:i:17:p:5179-5197
    DOI: 10.1080/00207543.2020.1774678
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