Data-Driven Based State Transition Algorithm for Dynamic Optimization
Most evolutionary algorithms solve the optimization problems by iteratively calculating the values of the objective functions. However, such explicit objective functions may not exist in solving many process industry optimization problems, especially when these processes have time varying dynamics,...
Uloženo v:
| Vydáno v: | 2019 IEEE Symposium Series on Computational Intelligence (SSCI) s. 1091 - 1096 |
|---|---|
| Hlavní autoři: | , , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina |
| Vydáno: |
IEEE
01.12.2019
|
| Témata: | |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Most evolutionary algorithms solve the optimization problems by iteratively calculating the values of the objective functions. However, such explicit objective functions may not exist in solving many process industry optimization problems, especially when these processes have time varying dynamics, instead costly physical experiments should be conducted to evaluate the fitness values, which hinders the application of evolutionary algorithms. In this paper, a novel dynamic optimization technique based on data-driven state transition algorithm (STA) is investigated to solve the aforementioned issue. Firstly, the control vector parameterization (CVP) method is used to discretize the control variables, and control variable vectors are used to approximate the control trajectories. By means of costly experiments, data pairs are obtained by combining variable vectors and their corresponding fitness. Then, a novel method is proposed to obtain the optimal variable vector based on data-driven STA. By applying it in two well-known dynamic optimization instances, simulation results demonstrate that the proposed approach is able to obtain better optimization results than other methods with a limited budget of exact function evaluations. |
|---|---|
| AbstractList | Most evolutionary algorithms solve the optimization problems by iteratively calculating the values of the objective functions. However, such explicit objective functions may not exist in solving many process industry optimization problems, especially when these processes have time varying dynamics, instead costly physical experiments should be conducted to evaluate the fitness values, which hinders the application of evolutionary algorithms. In this paper, a novel dynamic optimization technique based on data-driven state transition algorithm (STA) is investigated to solve the aforementioned issue. Firstly, the control vector parameterization (CVP) method is used to discretize the control variables, and control variable vectors are used to approximate the control trajectories. By means of costly experiments, data pairs are obtained by combining variable vectors and their corresponding fitness. Then, a novel method is proposed to obtain the optimal variable vector based on data-driven STA. By applying it in two well-known dynamic optimization instances, simulation results demonstrate that the proposed approach is able to obtain better optimization results than other methods with a limited budget of exact function evaluations. |
| Author | Yang, Chunhua Zhou, Xiaojun Zhang, Yunxiang |
| Author_xml | – sequence: 1 givenname: Yunxiang surname: Zhang fullname: Zhang, Yunxiang organization: Central South University,School of Automation,Changsha,P. R. China,410083 – sequence: 2 givenname: Xiaojun surname: Zhou fullname: Zhou, Xiaojun organization: Central South University,School of Automation,Changsha,P. R. China,410083 – sequence: 3 givenname: Chunhua surname: Yang fullname: Yang, Chunhua organization: Central South University,School of Automation,Changsha,P. R. China,410083 |
| BookMark | eNotj8FKAzEURSPoQmu_QJD8wNS8ZMbkLetMrYVCF1PX5cW-aKAzUzJBqF9vxa4uXA6Xc-_EdT_0LMQjqBmAwqe2rVdl6cDOtAKcoVJGObgSU7TnUjvQpavcrVg0lKloUvzmXr7QyHvZZsost4n6MeY49HJ--BxSzF-dDEOSzamnLn7IzTHHLv7QH3IvbgIdRp5eciLeXxfb-q1Yb5arer4uIoDLBeqKgkfN9iwFVPlgiDEYDR6dUwH0PiAjolNsKrLWe2OePUJFQMqCmYiH_93IzLtjih2l0-5yzvwCcXZI-A |
| ContentType | Conference Proceeding |
| DBID | 6IE 6IL CBEJK RIE RIL |
| DOI | 10.1109/SSCI44817.2019.9003081 |
| DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Electronic Library (IEL) IEEE Proceedings Order Plans (POP All) 1998-Present |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| EISBN | 9781728124858 1728124859 |
| EndPage | 1096 |
| ExternalDocumentID | 9003081 |
| Genre | orig-research |
| GroupedDBID | 6IE 6IL CBEJK RIE RIL |
| ID | FETCH-LOGICAL-i118t-925afb92e72011a5bf3ae9f321b9880f12df9e99980e35a77bb336b915a1a0713 |
| IEDL.DBID | RIE |
| IngestDate | Thu Jun 29 18:38:34 EDT 2023 |
| IsPeerReviewed | false |
| IsScholarly | false |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-i118t-925afb92e72011a5bf3ae9f321b9880f12df9e99980e35a77bb336b915a1a0713 |
| PageCount | 6 |
| ParticipantIDs | ieee_primary_9003081 |
| PublicationCentury | 2000 |
| PublicationDate | 2019-Dec. |
| PublicationDateYYYYMMDD | 2019-12-01 |
| PublicationDate_xml | – month: 12 year: 2019 text: 2019-Dec. |
| PublicationDecade | 2010 |
| PublicationTitle | 2019 IEEE Symposium Series on Computational Intelligence (SSCI) |
| PublicationTitleAbbrev | SSCI |
| PublicationYear | 2019 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| Score | 1.7068487 |
| Snippet | Most evolutionary algorithms solve the optimization problems by iteratively calculating the values of the objective functions. However, such explicit objective... |
| SourceID | ieee |
| SourceType | Publisher |
| StartPage | 1091 |
| SubjectTerms | Approximation algorithms Control vector parametrization Data-driven Dynamic optimization Evolutionary computation Heuristic algorithms Linear programming Optimization Process control State transition algorithm |
| Title | Data-Driven Based State Transition Algorithm for Dynamic Optimization |
| URI | https://ieeexplore.ieee.org/document/9003081 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1NSwMxEA21ePCk0orf5ODRtJuN2TRH7Qd6qYUq9FaSzUQLtlvq1t_vJLtUBC_ewhIImyXz3tvMmyHkRoKwd9JmLBMuZ-FijmnrLKpWh8FPJXlSN5tQ43FvNtOTBrndeWEAICafQScM412-K_Jt-FXW1bG6CmqdPaWyyqtVm355orvTaf8JxQZXIWFLd-rJv7qmRNAYHf5vuSPS_nHf0ckOV45JA1YtMhyY0rDBJgQn-oDQ42jkiTSCTcy7ovcfbwVq_fclRSZKB1WvefqMQWFZuy3b5HU0fOk_sroFAlsg8y-ZTqXxVqegAlAbab0woL1IudV48jxPndeAJK-XgJBGKWuFyKzm0nATBOgJaa6KFZwSGgrFAfInnwmBKk7bXpqD0BgNuffg5BlphS2Yr6sqF_P67c__fnxBDsIuV4kdl6RZbrZwRfbzr3LxubmOn-YbtMuP9Q |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1dS8MwFA1jCvqksonf5sFHszWNaZdH3QcbzjnYhL2NpLnRgdtkdv5-b7IyEXzxrZRCaUrvOae55x5CbiQIcydNwhJhM-Y35pgy1qBqtVj80iiLirCJdDBoTCZqWCK3Wy8MAITmM6j5w7CXb5fZ2v8qq6swXQW1zo5PzircWoXtl0eqPho1eyg3eOpbtlStuPxXbkqAjc7B_254SKo__js63CLLESnBokLaLZ1r1lr58kQfEHwsDUyRBrgJnVf0_v11iWr_bU6Ri9LWJm2ePmNZmBd-yyp56bTHzS4rQhDYDLl_zlQstTMqhtRDtZbGCQ3KiZgbhd-e47F1CpDmNSIQUqepMUIkRnGpufYS9JiUF8sFnBDqR8UBMiiXCIE6TplGnIFQWA-5c2DlKan4JZh-bOZcTIunP_v79DXZ646f-tN-b_B4Tvb9im_aPC5IOV-t4ZLsZl_57HN1FV7TN2ahkz4 |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2019+IEEE+Symposium+Series+on+Computational+Intelligence+%28SSCI%29&rft.atitle=Data-Driven+Based+State+Transition+Algorithm+for+Dynamic+Optimization&rft.au=Zhang%2C+Yunxiang&rft.au=Zhou%2C+Xiaojun&rft.au=Yang%2C+Chunhua&rft.date=2019-12-01&rft.pub=IEEE&rft.spage=1091&rft.epage=1096&rft_id=info:doi/10.1109%2FSSCI44817.2019.9003081&rft.externalDocID=9003081 |