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

Liu, Genggeng; Yang, Liliang; Xu, Saijuan; Li, Zuoyong; Chen, Yeh-Cheng; Chen, Chi‐Hua

doi:10.7717/peerj-cs.473

Cited by 13 publications

(4 citation statements)

References 51 publications

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“…Due to the large scale of the problem, we set M = 20, and iter = 400. The inertia weight factor decreases linearly from 0.95 to 0.4, the learning factor c 1 decreases linearly from 0.9 to 0.15, and the learning factor c 2 increases linearly from 0.4 to 0.9 [42]. The parameter settings in refining strategy are consistent with those in [29].…”

Section: Expeimental Resultsmentioning

confidence: 53%

SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization

Liu

Jing

Zhou

et al. 2023

Preprint

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In the modern Very Large Scale Integration (VLSI) circuit design, the short-circuit problem is one of the key factors affecting routability. Due to the increased circuit size and net density, the short-circuit problem grows significantly in detailed routing. Furthermore, global routing ignores many problems in detailed routing, making for an increased mismatch between global routing and detailed routing. As a crucial intermediate phase between global routing and detailed routing, track assignment is an excellent phase to pre-process the short-circuit problem. However, existing track assignment algorithms face the problem of falling into local optimality. As one of the typical representatives of the swarm intelligence techniques, Particle Swarm Optimization (PSO) is a powerful tool to solve large-scale discrete problems. Therefore, we propose an effective Track Assignment Algorithm Based on Social Learning Discrete Particle Swarm Optimization (SLDPSO-TA). First, the proposed algorithm considers local nets to better guide detailed routing and an effective encoding method is designed to adapt the evolutionary algorithms better. Second, the social learning mode based on example pool mechanism is presented to improve the algorithm performance. Finally, a negotiation-based refining strategy is utilized to further reduce the overlap. Experimental results show that SLDPSO-TA can obtain the best overlap cost optimization among similar works, and reduce the congestion in key routing areas.

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Section: Expeimental Resultsmentioning

confidence: 53%

SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization

Liu

Jing

Zhou

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…The simulation results of industrial circuits show that this method can obtain high-quality routing solutions. [29] presented an XSMT construction method based on multistrategy optimization Discrete Differential Evolution (DDE), which significantly reduces XSMT wire length. [30] proposed an XSMT algorithm based on social learning DPSO and an effective twostage construction method to achieve the best wire length optimization effect.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Deep Reinforcement Learning-Based Global Router for VLSI Design

Yang

Liu

2023

Wireless Communications and Mobile Computing

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Global routing is a crucial step in the design of Very Large-Scale Integration (VLSI) circuits. However, most of the existing methods are heuristic algorithms, which cannot conjointly optimize the subproblems of global routing, resulting in congestion and overflow. In response to this challenge, an enhanced Deep Reinforcement Learning- (DRL-) based global router has been proposed, which comprises the following effective strategies. First, to avoid the overestimation problem generated by Q -learning, the proposed global router adopts the Double Deep Q -Network (DDQN) model. The DDQN-based global router has better performance in wire length optimization and convergence. Second, to avoid the agent from learning redundant information, an action elimination method is added to the action selection part, which significantly enhances the convergence performance of the training process. Third, to avoid the unfair allocation problem of routing resources in serial training, concurrent training is proposed to enhance the routability. Fourth, to reduce wire length and disperse routing resources, a new reward function is proposed to guide the agent to learn better routing solutions regarding wire length and congestion standard deviation. Experimental results demonstrate that the proposed algorithm outperforms others in several important performance metrics, including wire length, convergence performance, routability, and congestion standard deviation. In conclusion, the proposed enhanced DRL-based global router is a promising approach for solving the global routing problem in VLSI design, which can achieve superior performance compared to the heuristic method and DRL-based global router.

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“…MAs are applied in many fields, such as feature selection ( Ghasemi et al, 2023b ), economic dispatch ( Ayedi, 2023 ) due to their simple structure, easy application, and no derivative information on OPs. However, more and more OPs need solving urgently as modern society evolves and the OPs are more and more complex ( Liu et al, 2021 ; Ghasemi et al, 2023c ). It is very necessary to develop an MA with higher efficiency, stronger universality, and scalability.…”

Section: Introductionmentioning

confidence: 99%

Multi-strategy synthetized equilibrium optimizer and application

Sun,

Zhang,

Jin

et al. 2024

PeerJ Computer Science

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Background Improvement on the updating equation of an algorithm is among the most improving techniques. Due to the lack of search ability, high computational complexity and poor operability of equilibrium optimizer (EO) in solving complex optimization problems, an improved EO is proposed in this article, namely the multi-strategy on updating synthetized EO (MS-EO). Method Firstly, a simplified updating strategy is adopted in EO to improve operability and reduce computational complexity. Secondly, an information sharing strategy updates the concentrations in the early iterative stage using a dynamic tuning strategy in the simplified EO to form a simplified sharing EO (SS-EO) and enhance the exploration ability. Thirdly, a migration strategy and a golden section strategy are used for a golden particle updating to construct a Golden SS-EO (GS-EO) and improve the search ability. Finally, an elite learning strategy is implemented for the worst particle updating in the late stage to form MS-EO and strengthen the exploitation ability. The strategies are embedded into EO to balance between exploration and exploitation by giving full play to their respective advantages. Result and Finding Experimental results on the complex functions from CEC2013 and CEC2017 test sets demonstrate that MS-EO outperforms EO and quite a few state-of-the-art algorithms in search ability, running speed and operability. The experimental results of feature selection on several datasets show that MS-EO also provides more advantages.

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

Cited by 13 publications

References 51 publications

SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization

SLDPSO-TA: Track Assignment Based on Social Learning Discrete Particle Swarm Optimization

An Enhanced Deep Reinforcement Learning-Based Global Router for VLSI Design

Multi-strategy synthetized equilibrium optimizer and application

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