Flowchart of LLSKSO. Article Swipe
Ruyi Dong (9038174)
,
Ran Cui (542718)
,
Zhennao Cai (4524565)
,
Ali Asghar Heidari (11828696)
,
Lei Liu (5074)
,
Yanan Liu (114059)
,
Huiling Chen (9525)
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0334348.g001
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1371/journal.pone.0334348.g001
Kernel Search Optimization (KSO) is characterized by insufficient accuracy in local search, which makes it difficult to achieve local optimization. Therefore, this paper proposes a Large Local Search Kernel Search Optimization (LLSKSO) to enhance the local optimization ability. LLSKSO achieves the performance improvement by introducing several strategies. First, the initial population is homogenized using the good point set mechanism. Then, the little dung beetle search mechanism of the Dung Beetle Optimizer (DBO) is introduced to enhance the local search capability of the KSO. Finally, the Cauchy-Gaussian mutation strategy is utilized to prevent the algorithm from falling into local traps. These three steps enable LLSKSO to achieve a dynamic balance between local and global search. In addition, to verify the performance and robustness of LLSKSO, comparison experiments between LLSKSO and 10 well-known algorithms are conducted on 50 benchmark test functions. From the statistical results of mean, best and variance of different algorithms, the LLSKSO algorithm outperforms the other algorithms. Finally, LLSKSO is applied to the engineering problem of carbon fiber drafting ratio optimization. Moreover, the experimental results obtained by LLSKSO yielded smaller line densities and greater strengths compared to other algorithms. LLSKSO achieves theoretical optima in 16 out of 20 high-dimensional benchmark functions, with an average CPU runtime reduced by 30% compared to baseline methods. Therefore, it can be shown that LLSKSO can be used as an effective optimization algorithm and engineering assistance.
Related Topics
Concepts
Local optimum
Flowchart
Local search (optimization)
Mathematical optimization
Computer science
Benchmark (surveying)
Robustness (evolution)
Population
Iterated local search
Algorithm
Set (abstract data type)
Kernel (algebra)
Optimization algorithm
Optimization problem
Search algorithm
Metaheuristic
Global optimization
Point (geometry)
Line search
Test case
Evolutionary algorithm
Computation
Metadata
- Type
- other
- OA Status
- green
- OpenAlex ID
- https://openalex.org/W7110961660
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W7110961660Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1371/journal.pone.0334348.g001Digital Object Identifier
- Title
-
Flowchart of LLSKSO.Work title
- Type
-
otherOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-26Full publication date if available
- Authors
-
Ruyi Dong (9038174), Ran Cui (542718), Zhennao Cai (4524565), Ali Asghar Heidari (11828696), Lei Liu (5074), Yanan Liu (114059), Huiling Chen (9525)List of authors in order
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- Concepts
-
Local optimum, Flowchart, Local search (optimization), Mathematical optimization, Computer science, Benchmark (surveying), Robustness (evolution), Population, Iterated local search, Algorithm, Set (abstract data type), Kernel (algebra), Optimization algorithm, Optimization problem, Search algorithm, Metaheuristic, Global optimization, Point (geometry), Line search, Test case, Evolutionary algorithm, ComputationTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
Full payload
| id | https://openalex.org/W7110961660 |
|---|---|
| doi | https://doi.org/10.1371/journal.pone.0334348.g001 |
| ids.openalex | https://openalex.org/W7110961660 |
| fwci | |
| type | other |
| title | Flowchart of LLSKSO. |
| biblio.issue | |
| biblio.volume | |
| biblio.last_page | |
| biblio.first_page | |
| is_xpac | False |
| apc_list | |
| apc_paid | |
| concepts[0].id | https://openalex.org/C141934464 |
| concepts[0].level | 2 |
| concepts[0].score | 0.7060653567314148 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q3305386 |
| concepts[0].display_name | Local optimum |
| concepts[1].id | https://openalex.org/C72041958 |
| concepts[1].level | 2 |
| concepts[1].score | 0.600671112537384 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q185092 |
| concepts[1].display_name | Flowchart |
| concepts[2].id | https://openalex.org/C135320971 |
| concepts[2].level | 2 |
| concepts[2].score | 0.5935034155845642 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q1868524 |
| concepts[2].display_name | Local search (optimization) |
| concepts[3].id | https://openalex.org/C126255220 |
| concepts[3].level | 1 |
| concepts[3].score | 0.5316342115402222 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q141495 |
| concepts[3].display_name | Mathematical optimization |
| concepts[4].id | https://openalex.org/C41008148 |
| concepts[4].level | 0 |
| concepts[4].score | 0.52309250831604 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[4].display_name | Computer science |
| concepts[5].id | https://openalex.org/C185798385 |
| concepts[5].level | 2 |
| concepts[5].score | 0.5219337940216064 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q1161707 |
| concepts[5].display_name | Benchmark (surveying) |
| concepts[6].id | https://openalex.org/C63479239 |
| concepts[6].level | 3 |
| concepts[6].score | 0.4692060053348541 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q7353546 |
| concepts[6].display_name | Robustness (evolution) |
| concepts[7].id | https://openalex.org/C2908647359 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4502372741699219 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q2625603 |
| concepts[7].display_name | Population |
| concepts[8].id | https://openalex.org/C124145224 |
| concepts[8].level | 3 |
| concepts[8].score | 0.43361201882362366 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q6094397 |
| concepts[8].display_name | Iterated local search |
| concepts[9].id | https://openalex.org/C11413529 |
| concepts[9].level | 1 |
| concepts[9].score | 0.42181387543678284 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q8366 |
| concepts[9].display_name | Algorithm |
| concepts[10].id | https://openalex.org/C177264268 |
| concepts[10].level | 2 |
| concepts[10].score | 0.3945918083190918 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q1514741 |
| concepts[10].display_name | Set (abstract data type) |
| concepts[11].id | https://openalex.org/C74193536 |
| concepts[11].level | 2 |
| concepts[11].score | 0.3687255382537842 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q574844 |
| concepts[11].display_name | Kernel (algebra) |
| concepts[12].id | https://openalex.org/C2987595161 |
| concepts[12].level | 2 |
| concepts[12].score | 0.3401426076889038 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q141495 |
| concepts[12].display_name | Optimization algorithm |
| concepts[13].id | https://openalex.org/C137836250 |
| concepts[13].level | 2 |
| concepts[13].score | 0.31070074439048767 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q984063 |
| concepts[13].display_name | Optimization problem |
| concepts[14].id | https://openalex.org/C125583679 |
| concepts[14].level | 2 |
| concepts[14].score | 0.3098527491092682 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q755673 |
| concepts[14].display_name | Search algorithm |
| concepts[15].id | https://openalex.org/C109718341 |
| concepts[15].level | 2 |
| concepts[15].score | 0.3041779398918152 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q1385229 |
| concepts[15].display_name | Metaheuristic |
| concepts[16].id | https://openalex.org/C164752517 |
| concepts[16].level | 2 |
| concepts[16].score | 0.3007151186466217 |
| concepts[16].wikidata | https://www.wikidata.org/wiki/Q5570875 |
| concepts[16].display_name | Global optimization |
| concepts[17].id | https://openalex.org/C28719098 |
| concepts[17].level | 2 |
| concepts[17].score | 0.28023549914360046 |
| concepts[17].wikidata | https://www.wikidata.org/wiki/Q44946 |
| concepts[17].display_name | Point (geometry) |
| concepts[18].id | https://openalex.org/C85522705 |
| concepts[18].level | 3 |
| concepts[18].score | 0.26620370149612427 |
| concepts[18].wikidata | https://www.wikidata.org/wiki/Q3278015 |
| concepts[18].display_name | Line search |
| concepts[19].id | https://openalex.org/C128942645 |
| concepts[19].level | 3 |
| concepts[19].score | 0.2556571364402771 |
| concepts[19].wikidata | https://www.wikidata.org/wiki/Q1568346 |
| concepts[19].display_name | Test case |
| concepts[20].id | https://openalex.org/C159149176 |
| concepts[20].level | 2 |
| concepts[20].score | 0.2543875277042389 |
| concepts[20].wikidata | https://www.wikidata.org/wiki/Q14489129 |
| concepts[20].display_name | Evolutionary algorithm |
| concepts[21].id | https://openalex.org/C45374587 |
| concepts[21].level | 2 |
| concepts[21].score | 0.25008323788642883 |
| concepts[21].wikidata | https://www.wikidata.org/wiki/Q12525525 |
| concepts[21].display_name | Computation |
| keywords[0].id | https://openalex.org/keywords/local-optimum |
| keywords[0].score | 0.7060653567314148 |
| keywords[0].display_name | Local optimum |
| keywords[1].id | https://openalex.org/keywords/flowchart |
| keywords[1].score | 0.600671112537384 |
| keywords[1].display_name | Flowchart |
| keywords[2].id | https://openalex.org/keywords/local-search |
| keywords[2].score | 0.5935034155845642 |
| keywords[2].display_name | Local search (optimization) |
| keywords[3].id | https://openalex.org/keywords/benchmark |
| keywords[3].score | 0.5219337940216064 |
| keywords[3].display_name | Benchmark (surveying) |
| keywords[4].id | https://openalex.org/keywords/robustness |
| keywords[4].score | 0.4692060053348541 |
| keywords[4].display_name | Robustness (evolution) |
| keywords[5].id | https://openalex.org/keywords/population |
| keywords[5].score | 0.4502372741699219 |
| keywords[5].display_name | Population |
| keywords[6].id | https://openalex.org/keywords/iterated-local-search |
| keywords[6].score | 0.43361201882362366 |
| keywords[6].display_name | Iterated local search |
| keywords[7].id | https://openalex.org/keywords/set |
| keywords[7].score | 0.3945918083190918 |
| keywords[7].display_name | Set (abstract data type) |
| language | |
| locations[0].id | pmh:oai:figshare.com:article/30727060 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4306400572 |
| locations[0].source.issn | |
| locations[0].source.type | repository |
| locations[0].source.is_oa | False |
| locations[0].source.issn_l | |
| locations[0].source.is_core | False |
| locations[0].source.is_in_doaj | False |
| locations[0].source.display_name | OPAL (Open@LaTrobe) (La Trobe University) |
| locations[0].source.host_organization | https://openalex.org/I196829312 |
| locations[0].source.host_organization_name | La Trobe University |
| locations[0].source.host_organization_lineage | https://openalex.org/I196829312 |
| locations[0].license | cc-by |
| locations[0].pdf_url | |
| locations[0].version | submittedVersion |
| locations[0].raw_type | Figure |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | False |
| locations[0].is_published | False |
| locations[0].raw_source_name | |
| locations[0].landing_page_url | |
| authorships[0].author.id | |
| authorships[0].author.orcid | |
| authorships[0].author.display_name | Ruyi Dong (9038174) |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Ruyi Dong (9038174) |
| authorships[0].is_corresponding | True |
| authorships[1].author.id | |
| authorships[1].author.orcid | |
| authorships[1].author.display_name | Ran Cui (542718) |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Ran Cui (542718) |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | |
| authorships[2].author.orcid | |
| authorships[2].author.display_name | Zhennao Cai (4524565) |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Zhennao Cai (4524565) |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | |
| authorships[3].author.orcid | |
| authorships[3].author.display_name | Ali Asghar Heidari (11828696) |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Ali Asghar Heidari (11828696) |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | |
| authorships[4].author.orcid | |
| authorships[4].author.display_name | Lei Liu (5074) |
| authorships[4].author_position | middle |
| authorships[4].raw_author_name | Lei Liu (5074) |
| authorships[4].is_corresponding | False |
| authorships[5].author.id | |
| authorships[5].author.orcid | |
| authorships[5].author.display_name | Yanan Liu (114059) |
| authorships[5].author_position | middle |
| authorships[5].raw_author_name | Yanan Liu (114059) |
| authorships[5].is_corresponding | False |
| authorships[6].author.id | |
| authorships[6].author.orcid | |
| authorships[6].author.display_name | Huiling Chen (9525) |
| authorships[6].author_position | last |
| authorships[6].raw_author_name | Huiling Chen (9525) |
| authorships[6].is_corresponding | False |
| has_content.pdf | False |
| has_content.grobid_xml | False |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | |
| open_access.oa_status | green |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-12-10T00:00:00 |
| display_name | Flowchart of LLSKSO. |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-12-10T02:49:46.989445 |
| primary_topic | |
| cited_by_count | 0 |
| locations_count | 1 |
| best_oa_location.id | pmh:oai:figshare.com:article/30727060 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4306400572 |
| best_oa_location.source.issn | |
| best_oa_location.source.type | repository |
| best_oa_location.source.is_oa | False |
| best_oa_location.source.issn_l | |
| best_oa_location.source.is_core | False |
| best_oa_location.source.is_in_doaj | False |
| best_oa_location.source.display_name | OPAL (Open@LaTrobe) (La Trobe University) |
| best_oa_location.source.host_organization | https://openalex.org/I196829312 |
| best_oa_location.source.host_organization_name | La Trobe University |
| best_oa_location.source.host_organization_lineage | https://openalex.org/I196829312 |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | |
| best_oa_location.version | submittedVersion |
| best_oa_location.raw_type | Figure |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | False |
| best_oa_location.is_published | False |
| best_oa_location.raw_source_name | |
| best_oa_location.landing_page_url | |
| primary_location.id | pmh:oai:figshare.com:article/30727060 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4306400572 |
| primary_location.source.issn | |
| primary_location.source.type | repository |
| primary_location.source.is_oa | False |
| primary_location.source.issn_l | |
| primary_location.source.is_core | False |
| primary_location.source.is_in_doaj | False |
| primary_location.source.display_name | OPAL (Open@LaTrobe) (La Trobe University) |
| primary_location.source.host_organization | https://openalex.org/I196829312 |
| primary_location.source.host_organization_name | La Trobe University |
| primary_location.source.host_organization_lineage | https://openalex.org/I196829312 |
| primary_location.license | cc-by |
| primary_location.pdf_url | |
| primary_location.version | submittedVersion |
| primary_location.raw_type | Figure |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | |
| publication_date | 2025-11-26 |
| publication_year | 2025 |
| referenced_works_count | 0 |
| abstract_inverted_index.a | 25, 107 |
| abstract_inverted_index.10 | 130 |
| abstract_inverted_index.16 | 196 |
| abstract_inverted_index.20 | 199 |
| abstract_inverted_index.50 | 136 |
| abstract_inverted_index.In | 115 |
| abstract_inverted_index.an | 204, 226 |
| abstract_inverted_index.as | 225 |
| abstract_inverted_index.be | 218, 223 |
| abstract_inverted_index.by | 7, 44, 178, 209 |
| abstract_inverted_index.in | 10, 195 |
| abstract_inverted_index.is | 5, 52, 73, 89, 161 |
| abstract_inverted_index.it | 15, 216 |
| abstract_inverted_index.of | 67, 81, 123, 144, 149, 167, 198 |
| abstract_inverted_index.on | 135 |
| abstract_inverted_index.to | 17, 33, 75, 91, 105, 117, 163, 188, 212 |
| abstract_inverted_index.30% | 210 |
| abstract_inverted_index.CPU | 206 |
| abstract_inverted_index.and | 112, 121, 129, 147, 184, 230 |
| abstract_inverted_index.are | 133 |
| abstract_inverted_index.can | 217, 222 |
| abstract_inverted_index.out | 197 |
| abstract_inverted_index.set | 58 |
| abstract_inverted_index.the | 35, 41, 49, 55, 61, 68, 77, 82, 85, 93, 119, 141, 152, 156, 164, 174 |
| abstract_inverted_index.Dung | 69 |
| abstract_inverted_index.From | 140 |
| abstract_inverted_index.KSO. | 83 |
| abstract_inverted_index.best | 146 |
| abstract_inverted_index.dung | 63 |
| abstract_inverted_index.from | 95 |
| abstract_inverted_index.good | 56 |
| abstract_inverted_index.into | 97 |
| abstract_inverted_index.line | 182 |
| abstract_inverted_index.test | 138 |
| abstract_inverted_index.that | 220 |
| abstract_inverted_index.this | 22 |
| abstract_inverted_index.used | 224 |
| abstract_inverted_index.with | 203 |
| abstract_inverted_index.(DBO) | 72 |
| abstract_inverted_index.(KSO) | 4 |
| abstract_inverted_index.<div> | 0 |
| abstract_inverted_index.Large | 26 |
| abstract_inverted_index.Local | 27 |
| abstract_inverted_index.Then, | 60 |
| abstract_inverted_index.These | 100 |
| abstract_inverted_index.fiber | 169 |
| abstract_inverted_index.local | 11, 19, 36, 78, 98, 111 |
| abstract_inverted_index.makes | 14 |
| abstract_inverted_index.mean, | 145 |
| abstract_inverted_index.other | 157, 189 |
| abstract_inverted_index.paper | 23 |
| abstract_inverted_index.point | 57 |
| abstract_inverted_index.ratio | 171 |
| abstract_inverted_index.shown | 219 |
| abstract_inverted_index.steps | 102 |
| abstract_inverted_index.three | 101 |
| abstract_inverted_index.using | 54 |
| abstract_inverted_index.which | 13 |
| abstract_inverted_index.</div> | 233 |
| abstract_inverted_index.Beetle | 70 |
| abstract_inverted_index.First, | 48 |
| abstract_inverted_index.Kernel | 1, 29 |
| abstract_inverted_index.LLSKSO | 39, 104, 128, 153, 160, 179, 191, 221 |
| abstract_inverted_index.Search | 2, 28, 30 |
| abstract_inverted_index.beetle | 64 |
| abstract_inverted_index.carbon | 168 |
| abstract_inverted_index.enable | 103 |
| abstract_inverted_index.global | 113 |
| abstract_inverted_index.little | 62 |
| abstract_inverted_index.optima | 194 |
| abstract_inverted_index.search | 65, 79 |
| abstract_inverted_index.traps. | 99 |
| abstract_inverted_index.verify | 118 |
| abstract_inverted_index.LLSKSO, | 124 |
| abstract_inverted_index.achieve | 18, 106 |
| abstract_inverted_index.applied | 162 |
| abstract_inverted_index.average | 205 |
| abstract_inverted_index.balance | 109 |
| abstract_inverted_index.between | 110, 127 |
| abstract_inverted_index.dynamic | 108 |
| abstract_inverted_index.enhance | 34, 76 |
| abstract_inverted_index.falling | 96 |
| abstract_inverted_index.greater | 185 |
| abstract_inverted_index.initial | 50 |
| abstract_inverted_index.prevent | 92 |
| abstract_inverted_index.problem | 166 |
| abstract_inverted_index.reduced | 208 |
| abstract_inverted_index.results | 143, 176 |
| abstract_inverted_index.runtime | 207 |
| abstract_inverted_index.search, | 12 |
| abstract_inverted_index.search. | 114 |
| abstract_inverted_index.several | 46 |
| abstract_inverted_index.smaller | 181 |
| abstract_inverted_index.yielded | 180 |
| abstract_inverted_index.(LLSKSO) | 32 |
| abstract_inverted_index.Finally, | 84, 159 |
| abstract_inverted_index.ability. | 38 |
| abstract_inverted_index.accuracy | 9 |
| abstract_inverted_index.achieves | 40, 192 |
| abstract_inverted_index.baseline | 213 |
| abstract_inverted_index.compared | 187, 211 |
| abstract_inverted_index.drafting | 170 |
| abstract_inverted_index.methods. | 214 |
| abstract_inverted_index.mutation | 87 |
| abstract_inverted_index.obtained | 177 |
| abstract_inverted_index.proposes | 24 |
| abstract_inverted_index.strategy | 88 |
| abstract_inverted_index.utilized | 90 |
| abstract_inverted_index.variance | 148 |
| abstract_inverted_index.Moreover, | 173 |
| abstract_inverted_index.Optimizer | 71 |
| abstract_inverted_index.addition, | 116 |
| abstract_inverted_index.algorithm | 94, 154, 229 |
| abstract_inverted_index.benchmark | 137, 201 |
| abstract_inverted_index.conducted | 134 |
| abstract_inverted_index.densities | 183 |
| abstract_inverted_index.different | 150 |
| abstract_inverted_index.difficult | 16 |
| abstract_inverted_index.effective | 227 |
| abstract_inverted_index.mechanism | 66 |
| abstract_inverted_index.strengths | 186 |
| abstract_inverted_index.Therefore, | 21, 215 |
| abstract_inverted_index.algorithms | 132 |
| abstract_inverted_index.capability | 80 |
| abstract_inverted_index.comparison | 125 |
| abstract_inverted_index.functions, | 202 |
| abstract_inverted_index.functions. | 139 |
| abstract_inverted_index.introduced | 74 |
| abstract_inverted_index.mechanism. | 59 |
| abstract_inverted_index.population | 51 |
| abstract_inverted_index.robustness | 122 |
| abstract_inverted_index.well-known | 131 |
| abstract_inverted_index.algorithms, | 151 |
| abstract_inverted_index.algorithms. | 158, 190 |
| abstract_inverted_index.assistance. | 232 |
| abstract_inverted_index.engineering | 165, 231 |
| abstract_inverted_index.experiments | 126 |
| abstract_inverted_index.homogenized | 53 |
| abstract_inverted_index.improvement | 43 |
| abstract_inverted_index.introducing | 45 |
| abstract_inverted_index.outperforms | 155 |
| abstract_inverted_index.performance | 42, 120 |
| abstract_inverted_index.statistical | 142 |
| abstract_inverted_index.strategies. | 47 |
| abstract_inverted_index.theoretical | 193 |
| abstract_inverted_index.Optimization | 3, 31 |
| abstract_inverted_index.experimental | 175 |
| abstract_inverted_index.insufficient | 8 |
| abstract_inverted_index.optimization | 37, 228 |
| abstract_inverted_index.characterized | 6 |
| abstract_inverted_index.optimization. | 20, 172 |
| abstract_inverted_index.Cauchy-Gaussian | 86 |
| abstract_inverted_index.high-dimensional | 200 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile |