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Accelerating supply chains with Ant Colony Optimization across a range of hardware solutions
Computers & Industrial Engineering ( IF 7.9 ) Pub Date : 2020-09-01 , DOI: 10.1016/j.cie.2020.106610
Ivars Dzalbs 1 , Tatiana Kalganova 1
Affiliation  

Abstract Ant Colony algorithm has been applied to various optimisation problems, however, most of the previous work on scaling and parallelism focuses on Travelling Salesman Problems (TSPs). Although useful for benchmarks and new idea comparison, the algorithmic dynamics do not always transfer to complex real-life problems, where additional meta-data is required during solution construction. This paper explores how the benchmark performance differs from real-world problems in the context of Ant Colony Optimization (ACO) and demonstrate that in order to generalise the findings, the algorithms have to be tested on both standard benchmarks and real-world applications. ACO and its scaling dynamics with two parallel ACO architectures – Independent Ant Colonies (IAC) and Parallel Ants (PA). Results showed that PA was able to reach a higher solution quality in fewer iterations as the number of parallel instances increased. Furthermore, speed performance was measured across three different hardware solutions – 16 core CPU, 68 core Xeon Phi and up to 4 Geforce GPUs. State of the art, ACO vectorisation techniques such as SS-Roulette were implemented using C++ and CUDA. Although excellent for routing simple TSPs, it was concluded that for complex real-world supply chain routing GPUs are not suitable due to meta-data access footprint required. Thus, our work demonstrates that the standard benchmarks are not suitable for generalised conclusions.

中文翻译:

通过 Ant Colony Optimization 跨一系列硬件解决方案加速供应链

摘要 蚁群算法已被应用于各种优化问题,然而,之前关于缩放和并行性的大部分工作都集中在旅行商问题(TSP)上。虽然对基准测试和新想法比较有用,但算法动态并不总是转移到复杂的现实生活中,在解决方案构建过程中需要额外的元数据。本文探讨了基准性能如何在蚁群优化 (ACO) 的背景下与现实世界的问题不同,并证明为了概括研究结果,必须在标准基准和现实世界应用程序上测试算法。ACO 及其两个并行 ACO 架构的扩展动态——独立蚁群 (IAC) 和并行蚂蚁 (PA)。结果表明,随着并行实例数量的增加,PA 能够在更少的迭代中达到更高的解决方案质量。此外,还测量了三种不同硬件解决方案的速度性能——16 核 CPU、68 核 Xeon Phi 和最多 4 个 Geforce GPU。最先进的 ACO 矢量化技术,例如 SS-Roulette 是使用 C++ 和 CUDA 实现的。尽管非常适合路由简单的 TSP,但得出的结论是,由于需要元数据访问足迹,因此对于复杂的现实世界供应链路由 GPU 并不合适。因此,我们的工作表明标准基准不适合广义结论。68 核至强融核和多达 4 个 Geforce GPU。最先进的 ACO 矢量化技术,例如 SS-Roulette 是使用 C++ 和 CUDA 实现的。尽管非常适合路由简单的 TSP,但得出的结论是,由于需要元数据访问足迹,因此对于复杂的现实世界供应链路由 GPU 并不合适。因此,我们的工作表明标准基准不适合广义结论。68 核至强融核和多达 4 个 Geforce GPU。最先进的 ACO 矢量化技术,如 SS-Roulette 是使用 C++ 和 CUDA 实现的。尽管非常适合路由简单的 TSP,但得出的结论是,由于需要元数据访问足迹,因此对于复杂的现实世界供应链路由 GPU 并不合适。因此,我们的工作表明标准基准不适合广义结论。
更新日期:2020-09-01
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