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author:

Liu, G. (Liu, G..) [1] | Chen, Z. (Chen, Z..) [2] | Zhuang, Z. (Zhuang, Z..) [3] | Guo, W. (Guo, W..) [4] | Chen, G. (Chen, G..) [5]

Indexed by:

Scopus

Abstract:

The Steiner minimal tree (SMT) problem is an NP-hard problem, which is the best connection model for a multi-terminal net in global routing problem. This paper presents a unified algorithm for octagonal and rectilinear SMT construction based on hybrid transformation strategy (HTS) and self-adapting particle swarm optimization. Firstly, an effective HTS is proposed to enlarge the search space and improve the convergence speed. Secondly, the proposed HTS in the evolutionary process may produce an ineffective solution, and consequently the crossover and mutation operators of genetic algorithm (GA) based on union-find sets is proposed. Thirdly, a self-adapting strategy that can adjust the acceleration coefficients is proposed to further improve the convergence and the quality of the proposed algorithm. Finally, the hybrid transformation can be applied to GA and the proposed algorithm can be applied to rectilinear architecture. To our best knowledge, the proposed algorithm is the first unified algorithm to solve the SMT construction under both octagonal and rectilinear architecture. The experimental results show that the proposed algorithm can efficiently provide a better solution for SMT problem both in octagonal and rectilinear architectures than others. Moreover, the algorithm can obtain several topologies of SMT, which is beneficial for optimizing congestion in VLSI global routing stage. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Keyword:

Hybrid transformation strategy; Octagonal steiner minimal tree; Particle swarm optimization; Rectilinear steiner minimal tree; Self-adapting strategy; Unified algorithm

Community:

  • [ 1 ] [Liu, G.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
  • [ 2 ] [Liu, G.]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, 350116, China
  • [ 3 ] [Liu, G.]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou, 350116, China
  • [ 4 ] [Chen, Z.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
  • [ 5 ] [Zhuang, Z.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
  • [ 6 ] [Guo, W.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China
  • [ 7 ] [Guo, W.]Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing, Fuzhou University, Fuzhou, 350116, China
  • [ 8 ] [Guo, W.]Key Laboratory of Spatial Data Mining and Information Sharing, Ministry of Education, Fuzhou, 350116, China
  • [ 9 ] [Chen, G.]College of Mathematics and Computer Science, Fuzhou University, Fuzhou, 350116, China

Reprint 's Address:

  • [Chen, G.]College of Mathematics and Computer Science, Fuzhou UniversityChina

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Source :

Soft Computing

ISSN: 1432-7643

Year: 2020

Issue: 6

Volume: 24

Page: 3943-3961

3 . 6 4 3

JCR@2020

3 . 1 0 0

JCR@2023

ESI HC Threshold:149

JCR Journal Grade:2

CAS Journal Grade:3

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 80

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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