S-ABC: A paradigm of service domain-oriented artificial bee colony algorithms for service selection and composition

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Journal Article
Future Generation Computer Systems: the international journal of grid computing: theory, methods and applications, 2017, 68, pp. 304-319
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With the rapid development of Cloud Computing, Big Data, Social Networks, and the Internet of Things, typical service optimization problems (SOPs) such as service selection, service composition and service resource scheduling in the service computing field have become more and more complicated due to the constant enrichment and dynamic aggregation of large number of services, as well as the unceasing variation of user requirements. Meanwhile, with the long-term development and evolution of business in many application domains, some service domain features (such as priori, correlation and similarity) are usually formed, which have strong influences on solving SOPs. Unfortunately, the existing research efforts on SOPs primarily concentrate on designing general algorithms for specific problems without considering the service domain features. This often leads to undesirable results of SOPs. Therefore, how to design a paradigm of service domain-oriented optimization algorithms with service domain features becomes a challenge for providing optimization strategies and algorithms to solve SOPs effectively. By considering the influences of service domain features on solving SOPs, this paper proposes a set of service domain-oriented artificial bee colony algorithms (S-ABC) based on the optimization mechanism of Artificial Bee Colony (ABC) method. Furthermore, by configuring the items and parameters of the S-ABC paradigm in detail, optimization algorithms for particular SOPs (e.g., service selection and composition) could be derived. In this paper, the superiority of our proposed S-ABC is verified through solving concurrent service selection and service composition problem. By exploiting the artificial bee colony algorithms for the optimization problems in service domains, this work makes novel contributions for solving SOPs, as well as extends the theory of the swarm intelligence optimization.
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