Accurate sub-swarms particle swarm optimization algorithm for service composition |
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| Affiliation: | 1. State Key Lab of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing 100876, China;2. EB Information Technology Co., Ltd., 7-8th Floor, Kunxun Masion, No. 9 Zhichun Road, Haidian District, Beijing 100191, China;3. The Export-Import Bank of China, No. 77, BeiHeYan Street, DongCheng District, BeiJing 100009, China;1. Service d’Anatomie et Cytologie Pathologiques, Université de Rennes 1, Université Bretagne Loire, Rennes, France;2. Unité Mixte de Recherche 6290-Institut de Génétique et Développement de Rennes, Rennes, France;3. Service d’Oncologie Médicale, Centre Eugène Marquis, Rennes, France;4. Ecole des Hautes Etudes en Santé Publique, Rennes, France;5. Service d’Urologie, Université de Rennes 1, Université Bretagne Loire, Rennes, France;6. Service de Génétique Somatique des Cancers, Université de Rennes 1, Université Bretagne Loire, Rennes, France;7. Service d’Oncologie Médicale, Centre Hospitalier Universitaire Saint-André, Bordeaux, France;8. Service d’Urologie, Centre Hospitalier Universitaire Pellegrin, Bordeaux, France;9. Service d’Anatomie et Cytologie Pathologiques, Centre Hospitalier Universitaire Pellegrin, Bordeaux, France;10. Service de Cytogénétique, Université de Rennes 1, Université Bretagne Loire, Rennes, France;1. Instituto Nacional del Carbón, INCAR-CSIC, Apartado 73, 33080 Oviedo, Spain;2. Instituto de Ciencia y Tecnología de Polímeros, ICTP-CSIC, C/Juan de la Cierva 3, 28006 Madrid, Spain;1. Department of Esophagogastric Surgery, Tokyo Medical Dental University, Japan;2. Center for Minimally Invasive Surgery, Tokyo Medical Dental University, Japan;3. Department of Surgical Oncology, Graduate School, Tokyo Medical Dental University, Japan;1. State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, China;2. Stony Brook University SUNY, United States |
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| Abstract: | Service composition (SC) generates various composite applications quickly by using a novel service interaction model. Before composing services together, the most important thing is to find optimal candidate service instances compliant with non-functional requirements. Particle swarm optimization (PSO) is known as an effective and efficient algorithm, which is widely used in this process. However, the premature convergence and diversity loss of PSO always results in suboptimal solutions. In this paper, we propose an accurate sub-swarms particle swarm optimization (ASPSO) algorithm by adopting parallel and serial niching techniques. The ASPSO algorithm locates optimal solutions by using sub-swarms searching grid cells in which the density of feasible solutions is high. Simulation results demonstrate that the proposed algorithm improves the accuracy of the standard PSO algorithm in searching the optimal solution of service selection problem. |
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| Keywords: | Service composition Particle swarm optimization Multi-constraint optimal service |
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