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X-architecture Steiner minimal tree algorithm based on multi-strategy optimization discrete differential evolution

机译:基于多策略优化离散差分演化的X架构施泰纳最小树算法

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Global routing is an important link in very large scale integration (VLSI) design. As the best model of global routing, X-architecture Steiner minimal tree (XSMT) has a good performance in wire length optimization. XSMT belongs to non-Manhattan structural model, and its construction process cannot be completed in polynomial time, so the generation of XSMT is an NP hard problem. In this paper, an X-architecture Steiner minimal tree algorithm based on multi-strategy optimization discrete differential evolution (XSMT-MoDDE) is proposed. Firstly, an effective encoding strategy, a fitness function of XSMT, and an initialization strategy of population are proposed to record the structure of XSMT, evaluate the cost of XSMT and obtain better initial particles, respectively. Secondly, elite selection and cloning strategy, multiple mutation strategies, and adaptive learning factor strategy are presented to improve the search process of discrete differential evolution algorithm. Thirdly, an effective refining strategy is proposed to further improve the quality of the final Steiner tree. Finally, the results of the comparative experiments prove that XSMT-MoDDE can get the shortest wire length so far, and achieve a better optimization degree in the larger-scale problem.
机译:全球路由是非常大规模集成(VLSI)设计中的一个重要环节。作为全局路由的最佳模型,X-Architecture Steiner最小树(XSMT)在线长度优化方面具有良好的性能。 XSMT属于非曼哈顿结构模型,其施工过程不能在多项式时间内完成,因此XSMT的生成是NP难题。本文提出了一种基于多策略优化离散差分演进(XSMT-MODDE)的基于多策略优化离散差分演进的X架构Steiner最小树算法。首先,提出了一种有效的编码策略,XSMT的健身功能,以及群体的初始化策略,以记录XSMT的结构,评估XSMT的成本并分别获得更好的初始颗粒。其次,提出了精英选择和克隆策略,多种突变策略和自适应学习因子策略,以改善离散差分演化算法的搜索过程。第三,提出了有效的炼油策略,以进一步提高最终施泰纳树的质量。最后,比较实验结果证明XSMT-Modde可以获得到目前为止的最短线长度,并在较大规模的问题中实现更好的优化程度。

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