Abstract
The X-architecture Steiner Minimum Tree (XSMT) is the best connection model of non-Manhattan multi-terminal nets in global routing, and it is an NP-hard problem. Particle Swarm Optimization (PSO), with its efficient searching ability and self-organizing ability, has become a powerful tool for constructing the XSMT. However, PSO is prone to fall into the local optimum due to its excessive exploitation intensity. To keep a smooth trade-off between exploitation and exploration capabilities of PSO, maintain the diversity of the population, and obtain a better solution, this paper proposes an XSMT algorithm based on Competitive Swarm Optimizer (called CSO-XSMT). The algorithm utilizes the methods of pairwise competition and roulette wheel selection to randomly select the learning objects of particles so as to enhance the exploration ability of the population and improve the algorithm performance. Meanwhile, to further reduce the wirelength of the Steiner tree, a refine strategy based on sharing edges is proposed, which adjusts the Steiner tree obtained by CSO to improve the quality of the final routing tree. Experimental results show that compared with other Steiner tree construction algorithms, the proposed algorithm has better wirelength optimization capability and superior stability.
This work was partially supported by the National Natural Science Foundation of China under Grants No. 61877010, No. 11501114, No. 11271002 and No. U1705262, State Key Laboratory of Computer Architecture (ICT,CAS) under Grant No. CARCHB202014, Fujian Natural Science Funds under Grant No. 2019J01243 and No. 2018J07005, Independent Innovation Fund between Tianjin University and Fuzhou University under Grant No. TF2021-8, and Fuzhou University under Grants No. GXRC-20060 and No. XRC-1544.
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Zhou, R., Liu, G., Guo, W., Wang, X. (2021). An X-Architecture SMT Algorithm Based on Competitive Swarm Optimizer. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds) Web Information Systems and Applications. WISA 2021. Lecture Notes in Computer Science(), vol 12999. Springer, Cham. https://doi.org/10.1007/978-3-030-87571-8_34
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