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Constraint Programming and Ant Colony System for the Component Deployment Problem

机译:组件部署问题的约束规划和蚁群系统

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摘要

Contemporary motor vehicles have increasing numbers of automated functions to augment the safety and comfort of a car. The automotive industry has to incorporate increasing numbers of processing units in the structure of cars to run the software that provides these functionalities. The software components often need access to sensors or mechanical devices which they are designed to operate. The result is a network of hardware units which can accommodate a limited number of software programs, each of which has to be assigned to a hardware unit. A prime goal of this deployment problem is to find software-to-hardware assignments that maximise the reliability of the system. In doing so, the assignments have to observe a number of constraints to be viable. This includes limited memory of a hardware unit, collocation of software components on the same hardware units, and communication between software components. Since the problem consists of many constraints with a significantly large search space, we investigate an ACO and constraint programming (CP) hybrid for this problem. We find that despite the large number of constraints, ACO on its own is the most effective method providing good solutions by also exploring infeasible regions.
机译:现代汽车具有越来越多的自动化功能,以增强汽车的安全性和舒适性。汽车工业必须在汽车结构中纳入越来越多的处理单元,才能运行提供这些功能的软件。软件组件通常需要访问其设计为运行的传感器或机械设备。结果是硬件单元网络可以容纳有限数量的软件程序,每个软件程序都必须分配给一个硬件单元。此部署问题的主要目标是找到最大程度地提高系统可靠性的软件到硬件分配。在这样做时,作业必须遵守许多约束条件才是可行的。这包括硬件单元的有限内存,同一硬件单元上软件组件的并置以及软件组件之间的通信。由于该问题包含许多约束,并且搜索空间很大,因此我们针对此问题研究了ACO和约束编程(CP)的混合体。我们发现,尽管存在很多限制,但ACO本身还是通过探索不可行的区域来提供良好解决方案的最有效方法。

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