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首页> 外文会议>2016 IEEE International Conference on Power System Technology >Graphic-based optimal network reconfiguration in CPU/GPU architectures using AGA-LS metaheuristics
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Graphic-based optimal network reconfiguration in CPU/GPU architectures using AGA-LS metaheuristics

机译：使用AGA-LS元启发式技术在CPU / GPU架构中基于图形的最佳网络重新配置

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In this paper, we address the Optimal Network Reconfiguration (ONR) problem that operates on standard configurations of electrical networks. The main objectives handled by the ONR are the minimization of power loss, the number of switching operations and the deviations of bus voltages from their rated values. Due to its multi-objective nature and combinatorial aspects, the ONR is considered as NP-hard. Hence approximate approaches are very promising in generating high quality solutions within a concurrential run time. An exploration of the recent literature reveal that CPU-based approaches are effective but time consuming is important specially for large scale bus-systems. We propose GPU-based Adaptive Greedy Approach Local Search (AGA-LS) algorithm, a stochastic local search metaheuristic that generates high quality solutions for numerous combinatorial optimization problems. A benchmarking testbed on an IEEE 33-bus radial distribution system illustrates the incentive behind using AGA-LS for solving the ONR. The AGA-LS approach outperforms the state-of-the-art approaches in terms of computational time.
机译：在本文中，我们解决了在网络的标准配置上运行的最佳网络重新配置（ONR）问题。 ONR处理的主要目标是最大程度地减少功率损耗，开关操作次数以及总线电压与其额定值的偏差。由于其多目标性质和组合方面，ONR被认为是NP-hard。因此，近似方法非常有希望在并发运行时间内生成高质量的解决方案。对最新文献的研究表明，基于CPU的方法是有效的，但是耗时对于大型总线系统尤其重要。我们提出了基于GPU的自适应贪婪方法局部搜索（AGA-LS）算法，该算法是一种随机的局部搜索元启发式算法，可为众多组合优化问题生成高质量的解决方案。在IEEE 33总线径向分配系统上进行的基准测试平台说明了使用AGA-LS解决ONR的动机。在计算时间方面，AGA-LS方法优于最新方法。

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