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

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.

Automated summary

Service composition is a core issue in cloud manufacturing, with this paper focusing on two types of correlations and proposing a mathematical model and a many-objective genetic algorithm based on correlation. Experiments show the effectiveness of the algorithm in eliminating infeasible search space and providing high QoS service composition solutions.