基于自适应PSO和混合转换策略的X结构Steiner最小树算法

doi:10.16451/j.cnki.issn1003-6059.201805002

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Abstract

X-architecture Steiner minimal tree (XSMT) problem is an NP-hard problem, and it is the best connection model for a multi-terminal net in non-Manhattan global routing problem. An XSMT construction algorithm based on hybrid transformation strategy and self-adapting particle swarm optimization(PSO) is proposed. Firstly, an effective hybrid transformation strategy is designed to enlarge the search space and enhance the convergence of the algorithm. Secondly, the crossover and mutation operators based on union-find sets and a self-adapting strategy to adjust the learning factors are proposed to satisfy the robustness of particle coding and further speed up the convergence of algorithm. The experimental results show that the proposed algorithm efficiently produces a better solution than others. Moreover, it obtains a series of XSMTs with different topology but same length. Thus, it provides a variety of options for global routing and opportunities to reduce congestion.

Key words： X-Architecture Steiner Tree Particle Swarm Optimization Hybrid Transformation Strategy Self-adapting Strategy

Received: 22 January 2018

ZTFLH:

TP 301

Corresponding Authors: GUO Wenzhong, Ph.D., professor. His research interests include computational intelligence and its application.

About author:: LIU Genggeng, Ph.D. , lecturer. His research interests include computational intelligence and its application.
CHEN Zhisheng, master student. His research interests include EDA design algorithm.
CHEN Guolong, Ph.D., professor. His research interests include artificial intelligence and network information security.

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	LIU Genggeng
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Cite this article:

LIU Genggeng,CHEN Zhisheng,GUO Wenzhong等. Self-adapting PSO Algorithm with Efficient Hybrid Transformation Strategy for X-Architecture Steiner Minimal Tree Construction Algorithm[J]. , 2018, 31(5): 398-408.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.201805002 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2018/V31/I5/398

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