Internet of things (IoT) will create new opportunities to build applications that better integrate real-time state of the industry. With Web services accomplishing similar function proliferated, industrial enterprises have to choose appropriate Web services according Quality of Service (QoS) properties. It introduces the problem of QoS-oriented service composition (QSC). This study formulates the QSC problem as a multi-criteria goal programming (MCGP) model, and develops a multi-population genetic algorithm (MGA) to solve the model. MCGP not only automatically assigns high quality Web services to combine a composite service, but also finds non inferior composite services by relaxing QoS constraints to satisfy users' QoS requirements. Empirical comparisons demonstrate MGA outperforms to the GA. Moreover, the experiments indicate MGA is capable to solve the largescale QSC problem in terms of efficiency and scalability.
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Li et al. (2014) studied this question.
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