Tuning Genetic Algorithm Parameters for Placement of Integrated Circuit Cells

Melikyan, Vazgen; Harutyunyan, Ashot; Davtyan, Vachagan; Revazyan, David; Harutyunyan, Gevorg

doi:10.1109/ewdts59469.2023.10297063

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Algorithm 5 Pseudocode For Mu1

Complexity Analysis1

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2024

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“…Ref. [42] proposes that crossover appears to be more efficient than the mutation in larger population sizes, and the mutation is more efficient in small populations. Taking into account that cache allocation should be highly real time, the population size needs to be scaled down to a smaller range.…”

Section: Algorithm 5 Pseudocode For Mumentioning

confidence: 99%

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Cache Optimization Methods Involving Node and Content Sharding of Blockchain in Internet of Vehicles

Zhao,

Ding

2024

Electronics

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Blockchain stands out in addressing the data security requirements of the Internet of Vehicles. However, blockchain has storage pressure that cannot be met by most existing nodes. The emergence of Mobile Edge Computing allows nodes closer to the users to undertake the caching and computation process. Although sharding can alleviate the storage pressure on blockchain nodes, frequent cross-shard communication can affect the overall performance of the blockchain. In this paper, combining the features of traffic flow with strong regional similarity as well as inter-node correlation, we propose two sharding methods based on the current Vehicle–Infrastructure–Clouds three-tier service model. The proposed Content Sharding method can optimize node caching and improve the cache-hitting ratio. The proposed node sharding method can effectively reduce the system service delay by assisting nodes to cache the whole blockchain together across the network.

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Section: Algorithm 5 Pseudocode For Mumentioning

confidence: 99%

“…Ref. [42] refers that a population size of less than about 1000 chromosomes has positive effects. Ref.…”

Section: Complexity Analysismentioning

confidence: 99%