FC-PACO-RM: A Parallel Method for Service Composition Optimal-Selection in Cloud Manufacturing System
FC-PACO-RM: A Parallel Method for Service Composition Optimal-Selection in Cloud Manufacturing System
复制标题
FC-PACO-RM：云制造系统中服务组合优化选择的并行方法
DOI：
10.1109/tii.2012.2232936
复制
发表时间：
2013-11-01
影响因子：
12.3
通讯作者：
Zhang, Lin
中科院分区：
文献类型：
--
作者：
Tao, Fei;LaiLi, Yuanjun;Zhang, Lin
为了在现代企业系统中实现制造资源和能力的全面共享、自由流通和交易、按需使用，云制造作为一种新的面向服务的制造范式最近被提出。与云计算相比，CMFG管理的服务不仅包括计算和软件资源和能力服务，还包括各种制造资源和能力服务。这些动态的服务使得专家系统的功能更加强大，成为传统服务的更高层次的延伸。因此，服务组合优化选择(SCOS)作为基于CMFG的专家系统实现的关键问题变得非常重要。供应链调度问题是一个典型的NP-Hard问题，具有动态性和不确定性的特点。在CMFG中，用传统的方法求解约束条件众多的大规模SCOS问题可能效率不高。为了克服这一缺陷，首先研究了多目标约束条件下CMFG的混沌优化问题，然后提出了一种新的并行智能算法，即基于全连接的反射迁移并行自适应混沌优化算法(FC-PACO-RM)。该算法采用轮盘赌轮盘选择和自适应混沌优化进行搜索，并在孤岛模型中采用全连接并行化和新的反射迁移方法进行高效决策。为了验证FC-PACO-RM算法的性能，对3种典型的并行算法和3种典型的并行算法进行了比较。实验结果表明，该方法能有效地解决CMFG中复杂的SCOS问题。
In order to realize the full-scale sharing, free circulation and transaction, and on-demand-use of manufacturing resource and capabilities in modern enterprise systems (ES), Cloud manufacturing (CMfg) as a new service-oriented manufacturing paradigm has been proposed recently. Compared with cloud computing, the services that are managed in CMfg include not only computational and software resource and capability service, but also various manufacturing resources and capability service. These various dynamic services make ES more powerful and to be a higher-level extension of traditional services. Thus, as a key issue for the implementation of CMfg-based ES, service composition optimal-selection (SCOS) is becoming very important. SCOS is a typical NP-hard problem with the characteristics of dynamic and uncertainty. Solving large scale SCOS problem with numerous constraints in CMfg by using the traditional methods might be inefficient. To overcome this shortcoming, the formulation of SCOS in CMfg with multiple objectives and constraints is investigated first, and then a novel parallel intelligent algorithm, namely full connection based parallel adaptive chaos optimization with reflex migration (FC-PACO-RM) is developed. In the algorithm, roulette wheel selection and adaptive chaos optimization are introduced for search purpose, while full-connection parallelization in island model and new reflex migration way are also developed for efficient decision. To validate the performance of FC-PACO-RM, comparisons with 3 serial algorithms and 7 typical parallel methods are conducted in three typical cases. The results demonstrate the effectiveness of the proposed method for addressing complex SCOS in CMfg.