Peer Review #1 of "X-architecture Steiner minimal tree algorithm based on multi-strategy optimization discrete differential evolution (v0.2)" Article Swipe
Genggeng Liu
,
Liliang Yang
,
Saijuan Xu
,
Zuoyong Li
,
Yeh-Cheng Chen
,
Chi‐Hua Chen
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.7287/peerj-cs.473v0.2/reviews/1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.7287/peerj-cs.473v0.2/reviews/1
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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Metadata
- Type
- peer-review
- Language
- en
- Landing Page
- https://doi.org/10.7287/peerj-cs.473v0.2/reviews/1
- https://peerj.com/articles/cs-473v0.2/submission
- OA Status
- gold
- References
- 48
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4205705389
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4205705389Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.7287/peerj-cs.473v0.2/reviews/1Digital Object Identifier
- Title
-
Peer Review #1 of "X-architecture Steiner minimal tree algorithm based on multi-strategy optimization discrete differential evolution (v0.2)"Work title
- Type
-
peer-reviewOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-04-13Full publication date if available
- Authors
-
Genggeng Liu, Liliang Yang, Saijuan Xu, Zuoyong Li, Yeh-Cheng Chen, Chi‐Hua ChenList of authors in order
- Landing page
-
https://doi.org/10.7287/peerj-cs.473v0.2/reviews/1Publisher landing page
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https://peerj.com/articles/cs-473v0.2/submissionDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://peerj.com/articles/cs-473v0.2/submissionDirect OA link when available
- Concepts
-
Steiner tree problem, Architecture, Differential (mechanical device), Algorithm, Computer science, Tree (set theory), Differential evolution, Mathematics, Theoretical computer science, Combinatorics, Engineering, Geography, Archaeology, Aerospace engineeringTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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48Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 13, 48, 94, 99, 126, 143, 146, 150, 153, 161, 174 |
| abstract_inverted_index.Tree | 22, 63 |
| abstract_inverted_index.Very | 7 |
| abstract_inverted_index.XSMT | 51, 102 |
| abstract_inverted_index.best | 14 |
| abstract_inverted_index.cost | 100 |
| abstract_inverted_index.far, | 166 |
| abstract_inverted_index.good | 26 |
| abstract_inverted_index.hard | 55 |
| abstract_inverted_index.link | 5 |
| abstract_inverted_index.that | 157 |
| abstract_inverted_index.this | 57 |
| abstract_inverted_index.wire | 29, 163 |
| abstract_inverted_index.Large | 8 |
| abstract_inverted_index.Scale | 9 |
| abstract_inverted_index.XSMT, | 83, 97 |
| abstract_inverted_index.based | 65 |
| abstract_inverted_index.elite | 109 |
| abstract_inverted_index.final | 147 |
| abstract_inverted_index.model | 15 |
| abstract_inverted_index.prove | 156 |
| abstract_inverted_index.time, | 46 |
| abstract_inverted_index.(VLSI) | 11 |
| abstract_inverted_index.(XSMT) | 23 |
| abstract_inverted_index.Global | 0 |
| abstract_inverted_index.better | 105, 170 |
| abstract_inverted_index.cannot | 41 |
| abstract_inverted_index.degree | 172 |
| abstract_inverted_index.factor | 120 |
| abstract_inverted_index.global | 17 |
| abstract_inverted_index.length | 30, 164 |
| abstract_inverted_index.model, | 36 |
| abstract_inverted_index.obtain | 104 |
| abstract_inverted_index.paper, | 58 |
| abstract_inverted_index.record | 93 |
| abstract_inverted_index.search | 127 |
| abstract_inverted_index.Minimal | 21, 62 |
| abstract_inverted_index.Steiner | 20, 61, 148 |
| abstract_inverted_index.achieve | 168 |
| abstract_inverted_index.belongs | 32 |
| abstract_inverted_index.cloning | 112 |
| abstract_inverted_index.fitness | 80 |
| abstract_inverted_index.further | 141 |
| abstract_inverted_index.improve | 125, 142 |
| abstract_inverted_index.initial | 106 |
| abstract_inverted_index.process | 40, 128 |
| abstract_inverted_index.quality | 144 |
| abstract_inverted_index.results | 151 |
| abstract_inverted_index.routing | 1 |
| abstract_inverted_index.Discrete | 69 |
| abstract_inverted_index.adaptive | 118 |
| abstract_inverted_index.discrete | 130 |
| abstract_inverted_index.encoding | 77 |
| abstract_inverted_index.evaluate | 98 |
| abstract_inverted_index.function | 81 |
| abstract_inverted_index.learning | 119 |
| abstract_inverted_index.multiple | 114 |
| abstract_inverted_index.mutation | 115 |
| abstract_inverted_index.problem. | 176 |
| abstract_inverted_index.proposed | 91, 139 |
| abstract_inverted_index.refining | 136 |
| abstract_inverted_index.routing, | 18 |
| abstract_inverted_index.shortest | 162 |
| abstract_inverted_index.strategy | 87, 121, 137 |
| abstract_inverted_index.Evolution | 71 |
| abstract_inverted_index.algorithm | 64 |
| abstract_inverted_index.completed | 43 |
| abstract_inverted_index.design.As | 12 |
| abstract_inverted_index.effective | 76, 135 |
| abstract_inverted_index.evolution | 132 |
| abstract_inverted_index.important | 4 |
| abstract_inverted_index.presented | 123 |
| abstract_inverted_index.selection | 110 |
| abstract_inverted_index.strategy, | 78, 113 |
| abstract_inverted_index.structure | 95 |
| abstract_inverted_index.XSMT-MoDDE | 158 |
| abstract_inverted_index.generation | 49 |
| abstract_inverted_index.particles, | 107 |
| abstract_inverted_index.polynomial | 45 |
| abstract_inverted_index.population | 89 |
| abstract_inverted_index.problem.In | 56 |
| abstract_inverted_index.structural | 35 |
| abstract_inverted_index.Integration | 10 |
| abstract_inverted_index.comparative | 154 |
| abstract_inverted_index.experiments | 155 |
| abstract_inverted_index.performance | 27 |
| abstract_inverted_index.strategies, | 116 |
| abstract_inverted_index.(XSMT-MoDDE) | 72 |
| abstract_inverted_index.Differential | 70 |
| abstract_inverted_index.construction | 39 |
| abstract_inverted_index.differential | 131 |
| abstract_inverted_index.larger-scale | 175 |
| abstract_inverted_index.optimization | 68, 171 |
| abstract_inverted_index.non-Manhattan | 34 |
| abstract_inverted_index.tree.Finally, | 149 |
| abstract_inverted_index.Multi-strategy | 67 |
| abstract_inverted_index.X-architecture | 19, 60 |
| abstract_inverted_index.initialization | 86 |
| abstract_inverted_index.optimization.XSMT | 31 |
| abstract_inverted_index.proposed.Firstly, | 74 |
| abstract_inverted_index.algorithm.Thirdly, | 133 |
| abstract_inverted_index.respectively.Secondly, | 108 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| citation_normalized_percentile |