Correlation-aware manufacturing service composition model using an extended flower pollination algorithm

Due to the emergence of cloud computing technology, many services with the same functionalities and different non-functionalities occur in cloud manufacturing system. Thus, manufacturing service composition optimisation is becoming increasingly important to meet customer demands, where this issue involves multi-objective optimisation. In this study, we propose a new manufacturing service composition model based on quality of service as well as considerations of crowdsourcing and service correlation. To address the problem of multi-objective optimisation, we employ an extended flower pollination algorithm (FPA) to obtain the optimal service composition solution, where it not only utilises the adaptive parameters but also integrates with genetic algorithm (GA). A case study was conducted to illustrate the practicality and effectiveness of the proposed method compared with GA, differential evolution algorithm, and basic FPA.