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Xie, Yongquan, Zhou, Zude, Pham, Duc Truong, Xu, Wenjun and Ji, Chunqian (2015) A Multiuser Manufacturing Resource Service Composition Method Based on the Bees Algorithm. Computational Intelligence and Neuroscience, 2015. pp. 1-13. doi:10.1155/2015/780352
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Text (Final published version)
Ji C (2015) Computational Intelligence and Neuroscience Article.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (2MB) | Preview |
Abstract
In order to realize an optimal resource service allocation in current open and service-oriented manufacturing model, multiuser resource service composition (RSC) is modeled as a combinational and constrained multiobjective problem. The model takes into account both subjective and objective quality of service (QoS) properties as representatives to evaluate a solution. The QoS properties aggregation and evaluation techniques are based on existing researches. The basic Bees Algorithm is tailored for finding a near optimal solution to the model, since the basic version is only proposed to find a desired solution in continuous domain and thus not suitable for solving the problem modeled in our study. Particular rules are designed for handling the constraints and finding Pareto optimality. In addition, the established model introduces a trusted service set to each user so that the algorithm could start by searching in the neighbor of more reliable service chains (known as seeds) than those randomly generated. The advantages of these techniques are validated by experiments in terms of success rate, searching speed, ability of avoiding ingenuity, and so forth. The results demonstrate the effectiveness of the proposed method in handling multiuser RSC problems.
| Item Type: | Article |
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| Article Type: | Article |
| Related URLs: | |
| Subjects: | Q Science > QA Mathematics > QA76 Computer software |
| Divisions: | Schools and Research Institutes > School of Business, Computing and Social Sciences |
| Research Priority Areas: | Applied Business & Technology |
| Depositing User: | Susan Turner |
| Date Deposited: | 29 Jan 2016 15:59 |
| Last Modified: | 31 Aug 2023 08:01 |
| URI: | https://eprints.glos.ac.uk/id/eprint/3048 |
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