A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for a SLA-Aware Service Composition Problem
For SLA-aware service composition problem (SSC), an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm optimization algorithm (HMDPSO) is also proposed in this paper. According to the characteristic of this problem, a particle updating strategy is des...
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| Vydáno v: | Mathematical problems in engineering Ročník 2014; číslo 1 |
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| Hlavní autoři: | , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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New York
Hindawi Publishing Corporation
01.01.2014
John Wiley & Sons, Inc |
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| ISSN: | 1024-123X, 1563-5147 |
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| Abstract | For SLA-aware service composition problem (SSC), an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm optimization algorithm (HMDPSO) is also proposed in this paper. According to the characteristic of this problem, a particle updating strategy is designed by introducing crossover operator. In order to restrain particle swarm’s premature convergence and increase its global search capacity, the swarm diversity indicator is introduced and a particle mutation strategy is proposed to increase the swarm diversity. To accelerate the process of obtaining the feasible particle position, a local search strategy based on constraint domination is proposed and incorporated into the proposed algorithm. At last, some parameters in the algorithm HMDPSO are analyzed and set with relative proper values, and then the algorithm HMDPSO and the algorithm HMDPSO+ incorporated by local search strategy are compared with the recently proposed related algorithms on different scale cases. The results show that algorithm HMDPSO+ can solve the SSC problem more effectively. |
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| AbstractList | For SLA-aware service composition problem (SSC), an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm optimization algorithm (HMDPSO) is also proposed in this paper. According to the characteristic of this problem, a particle updating strategy is designed by introducing crossover operator. In order to restrain particle swarm’s premature convergence and increase its global search capacity, the swarm diversity indicator is introduced and a particle mutation strategy is proposed to increase the swarm diversity. To accelerate the process of obtaining the feasible particle position, a local search strategy based on constraint domination is proposed and incorporated into the proposed algorithm. At last, some parameters in the algorithm HMDPSO are analyzed and set with relative proper values, and then the algorithm HMDPSO and the algorithm HMDPSO+ incorporated by local search strategy are compared with the recently proposed related algorithms on different scale cases. The results show that algorithm HMDPSO+ can solve the SSC problem more effectively. |
| Author | Zhang, Changsheng Zhang, Bin Yin, Hao Guo, Ying Liu, Tingting |
| Author_xml | – sequence: 1 givenname: Hao surname: Yin fullname: Yin, Hao organization: College of Information and Engineering Northeastern University Shenyang China neu.edu.cn – sequence: 2 givenname: Changsheng surname: Zhang fullname: Zhang, Changsheng organization: College of Information and Engineering Northeastern University Shenyang China neu.edu.cn – sequence: 3 givenname: Bin surname: Zhang fullname: Zhang, Bin organization: College of Information and Engineering Northeastern University Shenyang China neu.edu.cn – sequence: 4 givenname: Ying surname: Guo fullname: Guo, Ying organization: College of Information and Engineering Northeastern University Shenyang China neu.edu.cn – sequence: 5 givenname: Tingting surname: Liu fullname: Liu, Tingting organization: College of Information and Engineering Northeastern University Shenyang China neu.edu.cn |
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| CitedBy_id | crossref_primary_10_1002_cpe_5351 crossref_primary_10_1002_spe_2575 crossref_primary_10_1155_2015_807527 crossref_primary_10_1016_j_eswa_2016_10_047 crossref_primary_10_1155_2014_761403 crossref_primary_10_1109_ACCESS_2025_3554054 crossref_primary_10_1002_cpe_6362 crossref_primary_10_1016_j_future_2016_09_008 crossref_primary_10_1109_ACCESS_2021_3133505 crossref_primary_10_1109_TSC_2014_2365799 crossref_primary_10_1016_j_jobe_2025_113452 crossref_primary_10_1109_TETCI_2020_3027870 crossref_primary_10_1109_TETCI_2020_2969213 crossref_primary_10_1109_TSC_2015_2473840 crossref_primary_10_1016_j_infsof_2019_05_013 crossref_primary_10_1109_TEVC_2022_3170899 |
| Cites_doi | 10.1109/TSE.2010.92 10.3724/SP.J.1016.2012.00270 10.1109/4235.996017 10.1109/TEVC.2010.2077298 10.1109/TSC.2011.6 10.3724/SP.J.1004.2009.00332 10.3724/SP.J.1016.2010.02147 10.1145/1232722.1232728 10.3724/SP.J.1001.2013.04400 10.3724/SP.J.1001.2011.03842 10.1109/TSE.2011.68 |
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| Copyright | Copyright © 2014 Hao Yin et al. Copyright © 2014 Hao Yin et al. Hao Yin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
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| DOI | 10.1155/2014/252934 |
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| Snippet | For SLA-aware service composition problem (SSC), an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm... For SLA‐aware service composition problem (SSC), an optimization model for this algorithm is built, and a hybrid multiobjective discrete particle swarm... |
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| SubjectTerms | Algorithms Atoms & subatomic particles Candidates Composition Concrete Convergence Heuristic Linear programming Mathematical models Multiple objective analysis Mutation Mutations Objectives Optimization Optimization models Particle swarm optimization Position indicators Quality of service Search methods Searching Service oriented architecture Strategy Swarm intelligence |
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| Title | A Hybrid Multiobjective Discrete Particle Swarm Optimization Algorithm for a SLA-Aware Service Composition Problem |
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