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X-Architecture Steiner Minimal Tree Construction Based on Discrete Differential Evolution

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Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD 2019)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1074))

Abstract

As the best connection model for the multi-pin net of the non-Manhattan architecture global routing problem, the X-architecture Steiner Minimum Tree (XSMT) construction is a Non-deterministic Polynomial hard (NP-hard) problem. The Differential Evolution (DE) algorithm has shown good application effect in solving various NP-hard problems. For this reason, based on the idea of DE algorithm, this paper proposes an XSMT construction algorithm for solving this problem. First of all, because the traditional DE algorithm is designed for continuous problems, the optimization ability is limited in solving discrete problems. This paper proposes a novel crossover operator and mutation operator. At the same time, in order to maintain the effectiveness of the evolutionary algorithm, an Edge-to-Point coding strategy suitable for evolutionary algorithms is proposed to better preserve the optimal substructure of the population. Finally, in order to speed up the convergence speed and quality of the algorithm, this paper proposes an initial solution based on the minimum tree generation algorithm. Experiments show that the effectiveness of the proposed algorithm and related strategies can construct a high-quality XSMT solution.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (Nos. 61877010 and 11501114), and the Fujian Natural Science Funds (No.2019J01243).

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Correspondence to Genggeng Liu .

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Wu, H., Xu, S., Zhuang, Z., Liu, G. (2020). X-Architecture Steiner Minimal Tree Construction Based on Discrete Differential Evolution. In: Liu, Y., Wang, L., Zhao, L., Yu, Z. (eds) Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery. ICNC-FSKD 2019. Advances in Intelligent Systems and Computing, vol 1074. Springer, Cham. https://doi.org/10.1007/978-3-030-32456-8_47

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