With the increase of users in the Internet of Things services and cloud computing, the providers are encouraged to present services with different functional and nonfunctional features (service quality) in the service pool. Based on the demand and supply rules, alongside the considerable growth in the number of provided services, these cloud service providers are facing tough competition for improving their service quality. This competition creates a tough and complex NP-Hard process of selecting and mixing the simple services for providing composite cloud services. The method of selecting the appropriate services from the available pool and overcoming the limitation determine the importance of different service quality parameters; also focusing on dynamic features and the fast changes in service properties are the most important matters that require analysis. This study presents a moth-flame algorithm-based method with service quality-awareness in the Internet of Things after the analysis of different service composition solutions; the results of the study show that the proposed method has a good degree of convergence for combining services with proper optimization. Also, the results show that the quality of the composite services created by the proposed method is better than other similar optimization algorithms.
