Continuous Dynamic Constrained Optimization-The Challenges
Many real-world dynamic problems have constraints, and in certain cases not only the objective function changes over time, but also the constraints. However, there is no research in answering the question of whether current algorithms work well on continuous dynamic constrained optimization problems...
Gespeichert in:
| Veröffentlicht in: | IEEE transactions on evolutionary computation 2012-12, Vol.16 (6), p.769-786 |
|---|---|
| Hauptverfasser: | , |
| Format: | Artikel |
| Sprache: | eng |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 786 |
|---|---|
| container_issue | 6 |
| container_start_page | 769 |
| container_title | IEEE transactions on evolutionary computation |
| container_volume | 16 |
| creator | THANH NGUYEN, Trung XIN YAO |
| description | Many real-world dynamic problems have constraints, and in certain cases not only the objective function changes over time, but also the constraints. However, there is no research in answering the question of whether current algorithms work well on continuous dynamic constrained optimization problems (DCOPs), nor is there any benchmark problem that reflects the common characteristics of continuous DCOPs. This paper contributes to the task of closing this gap. We will present some investigations on the characteristics that might make DCOPs difficult to solve by some existing dynamic optimization (DO) and constraint handling (CH) algorithms. We will then introduce a set of benchmark problems with these characteristics and test several representative DO and CH strategies on these problems. The results confirm that DCOPs do have special characteristics that can significantly affect algorithm performance. The results also reveal some interesting observations where the presence or combination of different types of dynamics and constraints can make the problems easier to solve for certain types of algorithms. Based on the analyses of the results, a list of potential requirements that an algorithm should meet to solve DCOPs effectively will be proposed. |
| doi_str_mv | 10.1109/TEVC.2011.2180533 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_pasca</sourceid><recordid>TN_cdi_pascalfrancis_primary_26811915</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6148271</ieee_id><sourcerecordid>1266717112</sourcerecordid><originalsourceid>FETCH-LOGICAL-c399t-50f7d36fde8c4a8034724901d8afb2927d25ea2963ca0d9151a6f0e94a933a9e3</originalsourceid><addsrcrecordid>eNpdkE9LAzEQxYMoWKsfQLwsiOBlaybZTTbeZK1_oNBLFW9hzGZtyjZbN7uH-ulNafHgaYaZ33s8HiGXQCcAVN0tpu_lhFGACYOC5pwfkRGoDFJKmTiOOy1UKmXxcUrOQlhRClkOakTuy9b3zg_tEJLHrce1M0k8hb5D522VzDe9W7sf7F3r08XSJuUSm8b6LxvOyUmNTbAXhzkmb0_TRfmSzubPr-XDLDVcqT7NaS0rLurKFibDgvJMskxRqAqsP5lismK5RaYEN0grBTmgqKlVGSrOUVk-Jrd7303Xfg829HrtgrFNg97G2BqYEBIkAIvo9T901Q6dj-kixagSInpGCvaU6doQOlvrTefW2G01UL1rU-_a1Ls29aHNqLk5OGMw2NQdeuPCn5CJAiBmj9zVnnPW2r-3gKxgEvgvzfJ8Dw</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>1220966933</pqid></control><display><type>article</type><title>Continuous Dynamic Constrained Optimization-The Challenges</title><source>IEEE Electronic Library (IEL)</source><creator>THANH NGUYEN, Trung ; XIN YAO</creator><creatorcontrib>THANH NGUYEN, Trung ; XIN YAO</creatorcontrib><description>Many real-world dynamic problems have constraints, and in certain cases not only the objective function changes over time, but also the constraints. However, there is no research in answering the question of whether current algorithms work well on continuous dynamic constrained optimization problems (DCOPs), nor is there any benchmark problem that reflects the common characteristics of continuous DCOPs. This paper contributes to the task of closing this gap. We will present some investigations on the characteristics that might make DCOPs difficult to solve by some existing dynamic optimization (DO) and constraint handling (CH) algorithms. We will then introduce a set of benchmark problems with these characteristics and test several representative DO and CH strategies on these problems. The results confirm that DCOPs do have special characteristics that can significantly affect algorithm performance. The results also reveal some interesting observations where the presence or combination of different types of dynamics and constraints can make the problems easier to solve for certain types of algorithms. Based on the analyses of the results, a list of potential requirements that an algorithm should meet to solve DCOPs effectively will be proposed.</description><identifier>ISSN: 1089-778X</identifier><identifier>EISSN: 1941-0026</identifier><identifier>DOI: 10.1109/TEVC.2011.2180533</identifier><identifier>CODEN: ITEVF5</identifier><language>eng</language><publisher>New York, NY: IEEE</publisher><subject>Algorithm design and analysis ; Algorithms ; Applied sciences ; Benchmark problems ; Benchmark testing ; Benchmarking ; Computer science; control theory; systems ; Computer systems performance. Reliability ; constraint handling (CH) ; Constraints ; dynamic constraints ; dynamic environments ; dynamic optimization (DO) ; Dynamic tests ; Dynamics ; Educational institutions ; Equations ; evolutionary algorithms ; Exact sciences and technology ; Heuristic algorithms ; Mathematical programming ; Operational research and scientific management ; Operational research. Management science ; Operations research ; Optimization ; performance measures ; Shape ; Software ; Strategy ; Studies ; Tasks</subject><ispartof>IEEE transactions on evolutionary computation, 2012-12, Vol.16 (6), p.769-786</ispartof><rights>2014 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Dec 2012</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c399t-50f7d36fde8c4a8034724901d8afb2927d25ea2963ca0d9151a6f0e94a933a9e3</citedby><cites>FETCH-LOGICAL-c399t-50f7d36fde8c4a8034724901d8afb2927d25ea2963ca0d9151a6f0e94a933a9e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6148271$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=26811915$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>THANH NGUYEN, Trung</creatorcontrib><creatorcontrib>XIN YAO</creatorcontrib><title>Continuous Dynamic Constrained Optimization-The Challenges</title><title>IEEE transactions on evolutionary computation</title><addtitle>TEVC</addtitle><description>Many real-world dynamic problems have constraints, and in certain cases not only the objective function changes over time, but also the constraints. However, there is no research in answering the question of whether current algorithms work well on continuous dynamic constrained optimization problems (DCOPs), nor is there any benchmark problem that reflects the common characteristics of continuous DCOPs. This paper contributes to the task of closing this gap. We will present some investigations on the characteristics that might make DCOPs difficult to solve by some existing dynamic optimization (DO) and constraint handling (CH) algorithms. We will then introduce a set of benchmark problems with these characteristics and test several representative DO and CH strategies on these problems. The results confirm that DCOPs do have special characteristics that can significantly affect algorithm performance. The results also reveal some interesting observations where the presence or combination of different types of dynamics and constraints can make the problems easier to solve for certain types of algorithms. Based on the analyses of the results, a list of potential requirements that an algorithm should meet to solve DCOPs effectively will be proposed.</description><subject>Algorithm design and analysis</subject><subject>Algorithms</subject><subject>Applied sciences</subject><subject>Benchmark problems</subject><subject>Benchmark testing</subject><subject>Benchmarking</subject><subject>Computer science; control theory; systems</subject><subject>Computer systems performance. Reliability</subject><subject>constraint handling (CH)</subject><subject>Constraints</subject><subject>dynamic constraints</subject><subject>dynamic environments</subject><subject>dynamic optimization (DO)</subject><subject>Dynamic tests</subject><subject>Dynamics</subject><subject>Educational institutions</subject><subject>Equations</subject><subject>evolutionary algorithms</subject><subject>Exact sciences and technology</subject><subject>Heuristic algorithms</subject><subject>Mathematical programming</subject><subject>Operational research and scientific management</subject><subject>Operational research. Management science</subject><subject>Operations research</subject><subject>Optimization</subject><subject>performance measures</subject><subject>Shape</subject><subject>Software</subject><subject>Strategy</subject><subject>Studies</subject><subject>Tasks</subject><issn>1089-778X</issn><issn>1941-0026</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2012</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><recordid>eNpdkE9LAzEQxYMoWKsfQLwsiOBlaybZTTbeZK1_oNBLFW9hzGZtyjZbN7uH-ulNafHgaYaZ33s8HiGXQCcAVN0tpu_lhFGACYOC5pwfkRGoDFJKmTiOOy1UKmXxcUrOQlhRClkOakTuy9b3zg_tEJLHrce1M0k8hb5D522VzDe9W7sf7F3r08XSJuUSm8b6LxvOyUmNTbAXhzkmb0_TRfmSzubPr-XDLDVcqT7NaS0rLurKFibDgvJMskxRqAqsP5lismK5RaYEN0grBTmgqKlVGSrOUVk-Jrd7303Xfg829HrtgrFNg97G2BqYEBIkAIvo9T901Q6dj-kixagSInpGCvaU6doQOlvrTefW2G01UL1rU-_a1Ls29aHNqLk5OGMw2NQdeuPCn5CJAiBmj9zVnnPW2r-3gKxgEvgvzfJ8Dw</recordid><startdate>20121201</startdate><enddate>20121201</enddate><creator>THANH NGUYEN, Trung</creator><creator>XIN YAO</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>ESBDL</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7SP</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>F28</scope><scope>FR3</scope></search><sort><creationdate>20121201</creationdate><title>Continuous Dynamic Constrained Optimization-The Challenges</title><author>THANH NGUYEN, Trung ; XIN YAO</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c399t-50f7d36fde8c4a8034724901d8afb2927d25ea2963ca0d9151a6f0e94a933a9e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2012</creationdate><topic>Algorithm design and analysis</topic><topic>Algorithms</topic><topic>Applied sciences</topic><topic>Benchmark problems</topic><topic>Benchmark testing</topic><topic>Benchmarking</topic><topic>Computer science; control theory; systems</topic><topic>Computer systems performance. Reliability</topic><topic>constraint handling (CH)</topic><topic>Constraints</topic><topic>dynamic constraints</topic><topic>dynamic environments</topic><topic>dynamic optimization (DO)</topic><topic>Dynamic tests</topic><topic>Dynamics</topic><topic>Educational institutions</topic><topic>Equations</topic><topic>evolutionary algorithms</topic><topic>Exact sciences and technology</topic><topic>Heuristic algorithms</topic><topic>Mathematical programming</topic><topic>Operational research and scientific management</topic><topic>Operational research. Management science</topic><topic>Operations research</topic><topic>Optimization</topic><topic>performance measures</topic><topic>Shape</topic><topic>Software</topic><topic>Strategy</topic><topic>Studies</topic><topic>Tasks</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>THANH NGUYEN, Trung</creatorcontrib><creatorcontrib>XIN YAO</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE Open Access Journals</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Electronics & Communications Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ANTE: Abstracts in New Technology & Engineering</collection><collection>Engineering Research Database</collection><jtitle>IEEE transactions on evolutionary computation</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>THANH NGUYEN, Trung</au><au>XIN YAO</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Continuous Dynamic Constrained Optimization-The Challenges</atitle><jtitle>IEEE transactions on evolutionary computation</jtitle><stitle>TEVC</stitle><date>2012-12-01</date><risdate>2012</risdate><volume>16</volume><issue>6</issue><spage>769</spage><epage>786</epage><pages>769-786</pages><issn>1089-778X</issn><eissn>1941-0026</eissn><coden>ITEVF5</coden><abstract>Many real-world dynamic problems have constraints, and in certain cases not only the objective function changes over time, but also the constraints. However, there is no research in answering the question of whether current algorithms work well on continuous dynamic constrained optimization problems (DCOPs), nor is there any benchmark problem that reflects the common characteristics of continuous DCOPs. This paper contributes to the task of closing this gap. We will present some investigations on the characteristics that might make DCOPs difficult to solve by some existing dynamic optimization (DO) and constraint handling (CH) algorithms. We will then introduce a set of benchmark problems with these characteristics and test several representative DO and CH strategies on these problems. The results confirm that DCOPs do have special characteristics that can significantly affect algorithm performance. The results also reveal some interesting observations where the presence or combination of different types of dynamics and constraints can make the problems easier to solve for certain types of algorithms. Based on the analyses of the results, a list of potential requirements that an algorithm should meet to solve DCOPs effectively will be proposed.</abstract><cop>New York, NY</cop><pub>IEEE</pub><doi>10.1109/TEVC.2011.2180533</doi><tpages>18</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1089-778X |
| ispartof | IEEE transactions on evolutionary computation, 2012-12, Vol.16 (6), p.769-786 |
| issn | 1089-778X 1941-0026 |
| language | eng |
| recordid | cdi_pascalfrancis_primary_26811915 |
| source | IEEE Electronic Library (IEL) |
| subjects | Algorithm design and analysis Algorithms Applied sciences Benchmark problems Benchmark testing Benchmarking Computer science control theory systems Computer systems performance. Reliability constraint handling (CH) Constraints dynamic constraints dynamic environments dynamic optimization (DO) Dynamic tests Dynamics Educational institutions Equations evolutionary algorithms Exact sciences and technology Heuristic algorithms Mathematical programming Operational research and scientific management Operational research. Management science Operations research Optimization performance measures Shape Software Strategy Studies Tasks |
| title | Continuous Dynamic Constrained Optimization-The Challenges |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-30T22%3A27%3A19IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_pasca&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Continuous%20Dynamic%20Constrained%20Optimization-The%20Challenges&rft.jtitle=IEEE%20transactions%20on%20evolutionary%20computation&rft.au=THANH%20NGUYEN,%20Trung&rft.date=2012-12-01&rft.volume=16&rft.issue=6&rft.spage=769&rft.epage=786&rft.pages=769-786&rft.issn=1089-778X&rft.eissn=1941-0026&rft.coden=ITEVF5&rft_id=info:doi/10.1109/TEVC.2011.2180533&rft_dat=%3Cproquest_pasca%3E1266717112%3C/proquest_pasca%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=1220966933&rft_id=info:pmid/&rft_ieee_id=6148271&rfr_iscdi=true |