首页> 外文期刊>NeuroQuantology: an interdisciplinary journal of neuroscience and quantum physics >A Discrete Multi-Objective Optimization Method for Hardware/Software Partitioning Problem Based on Cuckoo Search and Elite Strategy
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A Discrete Multi-Objective Optimization Method for Hardware/Software Partitioning Problem Based on Cuckoo Search and Elite Strategy

机译:基于布谷鸟搜索和精英策略的离散多目标软硬件分区问题优化方法

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This paper attempts to provide a desirable solution to hardware/software partitioning of the embedded system. For this purpose, the author developed a discrete multi-objective optimization method based on the cuckoo search (CS) algorithm (MODCS) and the elite strategy of stratification and congestion degree comparison. Then, the MODCS was compared with two other typical simulation algorithms. The results show that the MODCS is superior to typical optimization algorithms in terms of many indices, including diversity, stability and generational distance (GD) of optimal solution. The superiority is positively correlated with the number of modules. The findings shed new light on the bionic optimization of hardware/software partitioning.
机译:本文试图为嵌入式系统的硬件/软件分区提供理想的解决方案。为此,作者开发了一种基于杜鹃搜索(CS)算法(MODCS)以及分层和拥塞程度比较的精英策略的离散多目标优化方法。然后,将MODCS与其他两种典型的仿真算法进行了比较。结果表明,MODCS在包括最优解的多样性,稳定性和世代距离(GD)在内的许多指标上均优于典型的优化算法。优势与模块数量成正相关。这些发现为仿生优化硬件/软件分区提供了新的思路。

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