Subset Approximation of Pareto Regions with Bi-objective A* Article Swipe
Nicolás Rivera
,
Jorge A. Baier
,
Carlos Hernández
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v36i9.21276
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1609/aaai.v36i9.21276
In bi-objective search, we are given a graph in which each directed arc is associated with a pair of non-negative weights, and the objective is to find the Pareto-optimal solution set. Unfortunately, in many practical settings, this set is too large, and therefore its computation is very time-consuming. In addition, even though bi-objective search algorithms generate the Pareto set incrementally, they do so exhaustively. This means that early during search the solution set covers is not diverse, being concentrated in a small region of the solution set. To address this issue, we present a new approach to subset approximation of the solution set, that can be used as the basis for an anytime bi-objective search algorithm. Our approach transforms the given task into a target bi-objective search task using two real parameters. For each particular parameter setting, the solutions to the target task is a subset of the solution set of the original task. Depending on the parameters used, the solution set of the target task may be computed very quickly. This allows us to obtain, in challenging road map benchmarks, a rich variety of solutions in times that may be orders of magnitude smaller than the time needed to compute the solution set. We show that by running the algorithm with an appropriate sequence of parameters, we obtain a growing sequence of solutions that converges to the full solution set. We prove that our approach is correct and that Bi-Objective A* prunes at least as many nodes when run over the target task.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1609/aaai.v36i9.21276
- https://ojs.aaai.org/index.php/AAAI/article/download/21276/21025
- OA Status
- diamond
- Cited By
- 6
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4283792718
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4283792718Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1609/aaai.v36i9.21276Digital Object Identifier
- Title
-
Subset Approximation of Pareto Regions with Bi-objective A*Work title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-06-28Full publication date if available
- Authors
-
Nicolás Rivera, Jorge A. Baier, Carlos HernándezList of authors in order
- Landing page
-
https://doi.org/10.1609/aaai.v36i9.21276Publisher landing page
- PDF URL
-
https://ojs.aaai.org/index.php/AAAI/article/download/21276/21025Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
- OA URL
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https://ojs.aaai.org/index.php/AAAI/article/download/21276/21025Direct OA link when available
- Concepts
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Solution set, Set (abstract data type), Pareto principle, Sequence (biology), Computation, Task (project management), Algorithm, Computer science, Mathematical optimization, Multi-objective optimization, Mathematics, Economics, Programming language, Management, Genetics, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
6Total citation count in OpenAlex
- Citations by year (recent)
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2024: 2, 2023: 2, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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21Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.the | 22, 27, 56, 70, 84, 100, 108, 119, 137, 140, 147, 151, 156, 159, 163, 196, 201, 209, 227, 251 |
| abstract_inverted_index.too | 39 |
| abstract_inverted_index.two | 129 |
| abstract_inverted_index.This | 64, 171 |
| abstract_inverted_index.each | 10, 133 |
| abstract_inverted_index.even | 50 |
| abstract_inverted_index.find | 26 |
| abstract_inverted_index.full | 228 |
| abstract_inverted_index.into | 122 |
| abstract_inverted_index.many | 33, 246 |
| abstract_inverted_index.over | 250 |
| abstract_inverted_index.pair | 17 |
| abstract_inverted_index.real | 130 |
| abstract_inverted_index.rich | 182 |
| abstract_inverted_index.road | 178 |
| abstract_inverted_index.set, | 102 |
| abstract_inverted_index.set. | 30, 86, 203, 230 |
| abstract_inverted_index.show | 205 |
| abstract_inverted_index.task | 121, 127, 142, 165 |
| abstract_inverted_index.than | 195 |
| abstract_inverted_index.that | 66, 103, 188, 206, 224, 233, 239 |
| abstract_inverted_index.they | 60 |
| abstract_inverted_index.this | 36, 89 |
| abstract_inverted_index.time | 197 |
| abstract_inverted_index.used | 106 |
| abstract_inverted_index.very | 46, 169 |
| abstract_inverted_index.when | 248 |
| abstract_inverted_index.with | 15, 211 |
| abstract_inverted_index.basis | 109 |
| abstract_inverted_index.being | 77 |
| abstract_inverted_index.early | 67 |
| abstract_inverted_index.given | 5, 120 |
| abstract_inverted_index.graph | 7 |
| abstract_inverted_index.least | 244 |
| abstract_inverted_index.means | 65 |
| abstract_inverted_index.nodes | 247 |
| abstract_inverted_index.prove | 232 |
| abstract_inverted_index.small | 81 |
| abstract_inverted_index.task. | 153, 253 |
| abstract_inverted_index.times | 187 |
| abstract_inverted_index.used, | 158 |
| abstract_inverted_index.using | 128 |
| abstract_inverted_index.which | 9 |
| abstract_inverted_index.Pareto | 57 |
| abstract_inverted_index.allows | 172 |
| abstract_inverted_index.covers | 73 |
| abstract_inverted_index.during | 68 |
| abstract_inverted_index.issue, | 90 |
| abstract_inverted_index.large, | 40 |
| abstract_inverted_index.needed | 198 |
| abstract_inverted_index.obtain | 218 |
| abstract_inverted_index.orders | 191 |
| abstract_inverted_index.prunes | 242 |
| abstract_inverted_index.region | 82 |
| abstract_inverted_index.search | 53, 69, 114, 126 |
| abstract_inverted_index.subset | 97, 145 |
| abstract_inverted_index.target | 124, 141, 164, 252 |
| abstract_inverted_index.though | 51 |
| abstract_inverted_index.address | 88 |
| abstract_inverted_index.anytime | 112 |
| abstract_inverted_index.compute | 200 |
| abstract_inverted_index.correct | 237 |
| abstract_inverted_index.growing | 220 |
| abstract_inverted_index.obtain, | 175 |
| abstract_inverted_index.present | 92 |
| abstract_inverted_index.running | 208 |
| abstract_inverted_index.search, | 2 |
| abstract_inverted_index.smaller | 194 |
| abstract_inverted_index.variety | 183 |
| abstract_inverted_index.approach | 95, 117, 235 |
| abstract_inverted_index.computed | 168 |
| abstract_inverted_index.directed | 11 |
| abstract_inverted_index.diverse, | 76 |
| abstract_inverted_index.generate | 55 |
| abstract_inverted_index.original | 152 |
| abstract_inverted_index.quickly. | 170 |
| abstract_inverted_index.sequence | 214, 221 |
| abstract_inverted_index.setting, | 136 |
| abstract_inverted_index.solution | 29, 71, 85, 101, 148, 160, 202, 229 |
| abstract_inverted_index.weights, | 20 |
| abstract_inverted_index.Depending | 154 |
| abstract_inverted_index.addition, | 49 |
| abstract_inverted_index.algorithm | 210 |
| abstract_inverted_index.converges | 225 |
| abstract_inverted_index.magnitude | 193 |
| abstract_inverted_index.objective | 23 |
| abstract_inverted_index.parameter | 135 |
| abstract_inverted_index.practical | 34 |
| abstract_inverted_index.settings, | 35 |
| abstract_inverted_index.solutions | 138, 185, 223 |
| abstract_inverted_index.therefore | 42 |
| abstract_inverted_index.algorithm. | 115 |
| abstract_inverted_index.algorithms | 54 |
| abstract_inverted_index.associated | 14 |
| abstract_inverted_index.parameters | 157 |
| abstract_inverted_index.particular | 134 |
| abstract_inverted_index.transforms | 118 |
| abstract_inverted_index.appropriate | 213 |
| abstract_inverted_index.benchmarks, | 180 |
| abstract_inverted_index.challenging | 177 |
| abstract_inverted_index.computation | 44 |
| abstract_inverted_index.parameters, | 216 |
| abstract_inverted_index.parameters. | 131 |
| abstract_inverted_index.Bi-Objective | 240 |
| abstract_inverted_index.bi-objective | 1, 52, 113, 125 |
| abstract_inverted_index.concentrated | 78 |
| abstract_inverted_index.non-negative | 19 |
| abstract_inverted_index.approximation | 98 |
| abstract_inverted_index.exhaustively. | 63 |
| abstract_inverted_index.Pareto-optimal | 28 |
| abstract_inverted_index.Unfortunately, | 31 |
| abstract_inverted_index.incrementally, | 59 |
| abstract_inverted_index.time-consuming. | 47 |
| cited_by_percentile_year.max | 96 |
| cited_by_percentile_year.min | 94 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.83552632 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |