Parallel surrogate-assisted global optimization with expensive functions – a survey
Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A...
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Article |
| Language: | English |
| Published: |
Springer Berlin Heidelberg
2016
|
| Online Access: | http://hdl.handle.net/1721.1/104932 https://orcid.org/0000-0002-2281-3067 |
| _version_ | 1826191146409263104 |
|---|---|
| author | Haftka, Raphael T. Villanueva, Diane Chaudhuri, Anirban |
| author2 | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics |
| author_facet | Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Haftka, Raphael T. Villanueva, Diane Chaudhuri, Anirban |
| author_sort | Haftka, Raphael T. |
| collection | MIT |
| description | Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A key issue focused on in this review is how different algorithms balance exploration and exploitation. Most of the papers surveyed are adaptive samplers that employ Gaussian Process or Kriging surrogates. These allow sophisticated approaches for balancing exploration and exploitation and even allow to develop algorithms with calculable rate of convergence as function of the number of parallel processors. In addition to optimization based on adaptive sampling, surrogate assisted parallel evolutionary algorithms are also surveyed. Beyond a review of the present state of the art, the paper also argues that methods that provide easy parallelization, like multiple parallel runs, or methods that rely on population of designs for diversity deserve more attention. |
| first_indexed | 2024-09-23T08:50:57Z |
| format | Article |
| id | mit-1721.1/104932 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:50:57Z |
| publishDate | 2016 |
| publisher | Springer Berlin Heidelberg |
| record_format | dspace |
| spelling | mit-1721.1/1049322022-09-30T11:43:42Z Parallel surrogate-assisted global optimization with expensive functions – a survey Haftka, Raphael T. Villanueva, Diane Chaudhuri, Anirban Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Chaudhuri, Anirban Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A key issue focused on in this review is how different algorithms balance exploration and exploitation. Most of the papers surveyed are adaptive samplers that employ Gaussian Process or Kriging surrogates. These allow sophisticated approaches for balancing exploration and exploitation and even allow to develop algorithms with calculable rate of convergence as function of the number of parallel processors. In addition to optimization based on adaptive sampling, surrogate assisted parallel evolutionary algorithms are also surveyed. Beyond a review of the present state of the art, the paper also argues that methods that provide easy parallelization, like multiple parallel runs, or methods that rely on population of designs for diversity deserve more attention. United States. Dept. of Energy (National Nuclear Security Administration. Advanced Simulation and Computing Program. Cooperative Agreement under the Predictive Academic Alliance Program. DE-NA0002378) 2016-10-21T22:19:47Z 2017-03-01T16:14:47Z 2016-04 2016-03 2016-08-18T15:23:35Z Article http://purl.org/eprint/type/JournalArticle 1615-147X 1615-1488 http://hdl.handle.net/1721.1/104932 Haftka, Raphael T., Diane Villanueva, and Anirban Chaudhuri. “Parallel Surrogate-Assisted Global Optimization with Expensive Functions – a Survey.” Structural and Multidisciplinary Optimization 54.1 (2016): 3–13. https://orcid.org/0000-0002-2281-3067 en http://dx.doi.org/10.1007/s00158-016-1432-3 Structural and Multidisciplinary Optimization Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. Springer-Verlag Berlin Heidelberg application/pdf Springer Berlin Heidelberg Springer Berlin Heidelberg |
| spellingShingle | Haftka, Raphael T. Villanueva, Diane Chaudhuri, Anirban Parallel surrogate-assisted global optimization with expensive functions – a survey |
| title | Parallel surrogate-assisted global optimization with expensive functions – a survey |
| title_full | Parallel surrogate-assisted global optimization with expensive functions – a survey |
| title_fullStr | Parallel surrogate-assisted global optimization with expensive functions – a survey |
| title_full_unstemmed | Parallel surrogate-assisted global optimization with expensive functions – a survey |
| title_short | Parallel surrogate-assisted global optimization with expensive functions – a survey |
| title_sort | parallel surrogate assisted global optimization with expensive functions a survey |
| url | http://hdl.handle.net/1721.1/104932 https://orcid.org/0000-0002-2281-3067 |
| work_keys_str_mv | AT haftkaraphaelt parallelsurrogateassistedglobaloptimizationwithexpensivefunctionsasurvey AT villanuevadiane parallelsurrogateassistedglobaloptimizationwithexpensivefunctionsasurvey AT chaudhurianirban parallelsurrogateassistedglobaloptimizationwithexpensivefunctionsasurvey |