Efficient VLSI routing algorithm employing novel discrete PSO and multi-stage transformation

Liu, Genggeng; Zhu, Weida; Xu, Saijuan; Zhuang, Zhen; Chen, Yeh-Cheng; Chen, Guolong

doi:10.1007/s12652-020-02659-8

Cited by 15 publications

(12 citation statements)

References 47 publications

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“… Liu et al (2015c) proposed a PSO-based multi-layer obstacle-avoiding XSMT, which used an effective penalty mechanism to help particles to avoid obstacles ( Liu et al, 2015c ). In Liu et al (2020b) , a novel DPSO and multi-stage transformation were used to construct XSMT and RSMT. The simulation results on industrial circuits showed that this method could obtain high-quality routing solutions ( Liu et al, 2020b ).…”

Section: Related Workmentioning

confidence: 99%

“…In Liu et al (2020b) , a novel DPSO and multi-stage transformation were used to construct XSMT and RSMT. The simulation results on industrial circuits showed that this method could obtain high-quality routing solutions ( Liu et al, 2020b ). Chen et al (2020b) proposed an XSMT construction algorithm based on Social Learning Particle Swarm Optimization (SLPSO), which can effectively balance the exploration and exploitation capabilities ( Chen et al, 2020b ).…”

Section: Related Workmentioning

confidence: 99%

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

Liu

Yang

et al. 2021

PeerJ Computer Science

Self Cite

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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.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

Liu

Yang

et al. 2021

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

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“…After the temperature is cooled, it can be sold the electronic parts on the circuit board. [17]. The intelligence expressed by arti cially manufactured machines can be called arti cial intelligence, which is usually realized by computer programs and algorithms.…”

Section: Manufacturingmentioning

confidence: 99%

“…Especially for the recognition of small objects, he proposed a two-stage object detection algorithm. , To reduce the over-tting situation, and nally use this automatic defect detection system to assist manual detection to reduce the missed detection rate and labor costs [17]. In 2016, Chen Weihan tried to solve the problem of balancing the workload of the production line, reducing the number of workstations to reduce enterprise costs, and maximizing the work e ciency of the production line, using genetic algorithms, immune algorithms, and particle swarm algorithms, and proposed a new The coding method is discussed and compared, and the results show that the solution speed of the particle swarm algorithm is better than the other two algorithms, and the solution quality of the immune algorithm is better than the other two algorithms [18].…”

Section: Application Of Arti Cial Intelligence In Manufacturingmentioning

confidence: 99%

“…If the label room personnel misunderstand, it will cause an error; when the label room personnel is set, it is di cult to clarify and test whether the old variables can be used, and whether the numbering logic assisted by the information personnel is wrong, it is di cult to clarify and test. If this part is not rigorous enough, errors may occur [17][18].…”

Section: Introductionmentioning

confidence: 99%

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Using AI technology to customize manufacture product label for decision making

Zhang

Dong

2021

Preprint

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When the manufacturing industry is dealing with information technology, it has to face a large number of parameters and frequent adjustments. How to correctly import and maintain it has always been a huge challenge. Once the setting is wrong, it will bring losses, ranging from poor products that require maintenance, heavy work or scrapping, and at worst, resulting in production line shutdown, reduced factory productivity, delayed shipments and other adverse consequences. In order to improve this problem, this study uses the data in the approval form of a customized label set by an electronic manufacturer and use artificial intelligence models to find out the hidden rules behind a large number of customized labels, through data processing and model building. Model and parameter experiments are used to improve the effectiveness of artificial intelligence models, and for the problem of time characteristics but uneven distribution of data, the method of cyclic testing is adopted to increase the diversity of the test set. The results of this paper, we integrate each stage and an auxiliary decision-making is established. When the user's setting is inconsistent with the predicted result, a warning will be displayed to speed up the operation process, reduce the scope of confirmation, and ultimately reduce the error rate, thereby improving the problem, reducing scrap and production line shutdown to improve factory productivity. In the statics, the accuracy rate of new recruits was only 80%. The accuracy rate of the artificial intelligence model can be increased to 95%. The number of stoppages is reduced from 4 times per month to 1 time per month. Under full capacity, this assistance the decision-making system can reduce loss cost.

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