Incremental Data-driven Optimization of Complex Systems in Nonstationary Environments Article Swipe
Cuie Yang
,
Jinliang Ding
,
Yaochu Jin
,
Tianyou Chai
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2012.07225
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2012.07225
Existing work on data-driven optimization focuses on problems in static environments, but little attention has been paid to problems in dynamic environments. This paper proposes a data-driven optimization algorithm to deal with the challenges presented by the dynamic environments. First, a data stream ensemble learning method is adopted to train the surrogates so that each base learner of the ensemble learns the time-varying objective function in the previous environments. After that, a multi-task evolutionary algorithm is employed to simultaneously optimize the problems in the past environments assisted by the ensemble surrogate. This way, the optimization tasks in the previous environments can be used to accelerate the tracking of the optimum in the current environment. Since the real fitness function is not available for verifying the surrogates in offline data-driven optimization, a support vector domain description that was designed for outlier detection is introduced to select a reliable solution. Empirical results on six dynamic optimization benchmark problems demonstrate the effectiveness of the proposed algorithm compared with four state-of-the-art data-driven optimization algorithms.
Related Topics
Concepts
Computer science
Benchmark (surveying)
Optimization problem
Task (project management)
Outlier
Data stream
Domain (mathematical analysis)
Machine learning
Fitness function
Artificial intelligence
Mathematical optimization
Algorithm
Genetic algorithm
Engineering
Telecommunications
Geodesy
Systems engineering
Mathematics
Geography
Mathematical analysis
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2012.07225
- https://arxiv.org/pdf/2012.07225
- OA Status
- green
- References
- 6
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2900917815
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2900917815Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.48550/arxiv.2012.07225Digital Object Identifier
- Title
-
Incremental Data-driven Optimization of Complex Systems in Nonstationary EnvironmentsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2020Year of publication
- Publication date
-
2020-12-14Full publication date if available
- Authors
-
Cuie Yang, Jinliang Ding, Yaochu Jin, Tianyou ChaiList of authors in order
- Landing page
-
https://arxiv.org/abs/2012.07225Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2012.07225Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2012.07225Direct OA link when available
- Concepts
-
Computer science, Benchmark (surveying), Optimization problem, Task (project management), Outlier, Data stream, Domain (mathematical analysis), Machine learning, Fitness function, Artificial intelligence, Mathematical optimization, Algorithm, Genetic algorithm, Engineering, Telecommunications, Geodesy, Systems engineering, Mathematics, Geography, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
6Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_lineage | https://openalex.org/I205783295 |
| primary_location.license | |
| primary_location.pdf_url | https://arxiv.org/pdf/2012.07225 |
| primary_location.version | submittedVersion |
| primary_location.raw_type | |
| primary_location.license_id | |
| primary_location.is_accepted | False |
| primary_location.is_published | False |
| primary_location.raw_source_name | |
| primary_location.landing_page_url | http://arxiv.org/abs/2012.07225 |
| publication_date | 2020-12-14 |
| publication_year | 2020 |
| referenced_works | https://openalex.org/W1543559221, https://openalex.org/W2336467679, https://openalex.org/W1904826605, https://openalex.org/W1595159159, https://openalex.org/W2113442785, https://openalex.org/W2604794100 |
| referenced_works_count | 6 |
| abstract_inverted_index.a | 25, 40, 71, 130, 145 |
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| abstract_inverted_index.is | 46, 75, 119, 141 |
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| abstract_inverted_index.for | 122, 138 |
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| abstract_inverted_index.ensemble | 43, 59, 89 |
| abstract_inverted_index.function | 64, 118 |
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| abstract_inverted_index.optimize | 79 |
| abstract_inverted_index.previous | 67, 98 |
| abstract_inverted_index.problems | 7, 18, 81, 155 |
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| abstract_inverted_index.Empirical | 148 |
| abstract_inverted_index.algorithm | 28, 74, 162 |
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| abstract_inverted_index.multi-task | 72 |
| abstract_inverted_index.surrogate. | 90 |
| abstract_inverted_index.surrogates | 51, 125 |
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| abstract_inverted_index.time-varying | 62 |
| abstract_inverted_index.effectiveness | 158 |
| abstract_inverted_index.environments, | 10 |
| abstract_inverted_index.environments. | 21, 38, 68 |
| abstract_inverted_index.optimization, | 129 |
| abstract_inverted_index.simultaneously | 78 |
| abstract_inverted_index.state-of-the-art | 166 |
| cited_by_percentile_year | |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile |