QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system
QoS and energy consumption aware service composition and optimal-selection based on Pareto group leader algorithm in cloud manufacturing system
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DOI：
10.1007/s10100-013-0293-8
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发表时间：
2013-04
影响因子：
1.7
通讯作者：
Feng Xiang;Yefa Hu;Yingrong Yu;Huachun Wu
中科院分区：
文献类型：
--
作者：
Feng Xiang;Yefa Hu;Yingrong Yu;Huachun Wu
服务组合与优化选择（SCOS）是实施云制造系统的关键问题之一。现有的SCOS工作主要是基于服务质量（QoS）为用户提供高质量的服务。在提供高质量和低能耗服务方面，几乎没有工作。因此，本文研究了基于QoS和能耗的SCOS（QoS-EnCon）问题。首先，建立了多目标服务组合模型，研究了服务质量和能耗的评价方法，提出了无量纲的服务质量目标函数。为了有效地解决多目标SCOS问题，提出了一种新的地球仪优化算法--组长算法（Group Leader Algorithm，GLA）。在GLA中，社会群体中领导者的影响力被用作进化技术的灵感，该技术被设计成群体架构。然后，从解的映射（即，一个组合服务执行路径）的SCOS问题的GLA解决方案，并提出了一种新的多目标优化算法（即，将Pareto解的思想与GLA相结合，提出了一种基于GLA-Pareto的SCOS问题求解方法。设计了实现Pareto-GA的关键算子。实例研究结果表明，与枚举法、遗传算法和粒子群优化算法相比，GLA-Pareto算法在解决云制造系统中的SCOS问题上具有更好的性能。
Service composition and optimal selection (SCOS) is one of the key issues for implementing a cloud manufacturing system. Exiting works on SCOS are primarily based on quality of service (QoS) to provide high-quality service for user. Few works have been delivered on providing both high-quality and low-energy consumption service. Therefore, this article studies the problem of SCOS based on QoS and energy consumption (QoS-EnCon). First, the model of multi-objective service composition was established; the evaluation of QoS and energy consumption (EnCon) were investigated, as well as a dimensionless QoS objective function. In order to solve the multi-objective SCOS problem effectively, then a novel globe optimization algorithm, namedgroup leader algorithm(GLA), was introduced. In GLA, the influence of the leaders in social groups is used as an inspiration for the evolutionary technology which is design into group architecture. Then, the mapping from the solution (i.e., a composed service execute path) of SCOS problem to a GLA solution is investigated, and a new multi-objective optimization algorithm (i.e., GLA-Pareto) based on the combination of the idea of Pareto solution and GLA is proposed for addressing the SCOS problem. The key operators for implementing the Pareto-GA are designed. The results of the case study illustrated that compared with enumeration method, genetic algorithm (GA), and particle swarm optimization, the proposed GLA-Pareto has better performance for addressing the SCOS problem in cloud manufacturing system.