Active Robust Optimization: Enhancing Robustness to Uncertain Environments

Many real world optimization problems involve uncertainties. A solution for such a problem is expected to be robust to these uncertainties. Commonly, robustness is attained by choosing the solution's parameters such that the solution's performance is less influenced by negative effects of...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:IEEE transactions on cybernetics Ročník 44; číslo 11; s. 2221 - 2231
Hlavní autoři: Salomon, Shaul, Avigad, Gideon, Fleming, Peter J., Purshouse, Robin C.
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.11.2014
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Adaptation
Adaptive design
Algorithms
Artificial Intelligence
Brightness
Computer Simulation
Cybernetics
Decision Support Techniques
dynamic optimization
Dynamic tests
Ecosystem
Equivalence
evolutionary algorithms
Expenses
Feedback
Linear programming
Models, Statistical
multiobjective optimization
Nonlinear programming
Optimization
Pareto optimization
Pattern Recognition, Automated - methods
robust optimization
Robustness
Tables (data)
Uncertainty
Vectors
multiobjective optimization
Adaptive design
dynamic optimization
evolutionary algorithms
robust optimization
ISSN:2168-2267, 2168-2275, 2168-2275
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract Many real world optimization problems involve uncertainties. A solution for such a problem is expected to be robust to these uncertainties. Commonly, robustness is attained by choosing the solution's parameters such that the solution's performance is less influenced by negative effects of the uncertain parameters' variations. This robustness may be viewed as a passive robustness, because once the solution's parameters are chosen, the robustness is inherent in the solution and no further action, to suppress the effect of uncertainties, is expected. However, it is acknowledged that enhanced robustness comes at the expense of peak performances. In this paper, active robust optimization is presented as a new robust optimization approach. It considers products that are able to adapt to environmental changes. The enhanced robustness of these solutions is attained by adaptation, which reduces the loss in performance due to environmental changes. A new optimization problem named active robust optimization problem is formulated. The problem amalgamates robust optimization with dynamic optimization to evaluate the performance of a candidate solution, while considering possible environmental conditions. The adaptation's influence on the solution's performance and cost is considered as well. Hence, the problem is formulated as a multiobjective problem that simultaneously aims at low costs and high performance. Since these goals are commonly in conflict, the solution is a set of optimal adaptive solutions. An evolutionary algorithm is proposed in order to evolve this set. An example of optimizing an adaptive optical table is provided. It is shown that an adaptive product, which is an outcome of the suggested approach, may be superior to an equivalent product that is not adaptive.
AbstractList Many real world optimization problems involve uncertainties. A solution for such a problem is expected to be robust to these uncertainties. Commonly, robustness is attained by choosing the solution's parameters such that the solution's performance is less influenced by negative effects of the uncertain parameters' variations. This robustness may be viewed as a passive robustness, because once the solution's parameters are chosen, the robustness is inherent in the solution and no further action, to suppress the effect of uncertainties, is expected. However, it is acknowledged that enhanced robustness comes at the expense of peak performances. In this paper, active robust optimization is presented as a new robust optimization approach. It considers products that are able to adapt to environmental changes. The enhanced robustness of these solutions is attained by adaptation, which reduces the loss in performance due to environmental changes. A new optimization problem named active robust optimization problem is formulated. The problem amalgamates robust optimization with dynamic optimization to evaluate the performance of a candidate solution, while considering possible environmental conditions. The adaptation's influence on the solution's performance and cost is considered as well. Hence, the problem is formulated as a multiobjective problem that simultaneously aims at low costs and high performance. Since these goals are commonly in conflict, the solution is a set of optimal adaptive solutions. An evolutionary algorithm is proposed in order to evolve this set. An example of optimizing an adaptive optical table is provided. It is shown that an adaptive product, which is an outcome of the suggested approach, may be superior to an equivalent product that is not adaptive.
Many real world optimization problems involve uncertainties. A solution for such a problem is expected to be robust to these uncertainties. Commonly, robustness is attained by choosing the solution's parameters such that the solution's performance is less influenced by negative effects of the uncertain parameters' variations. This robustness may be viewed as a passive robustness, because once the solution's parameters are chosen, the robustness is inherent in the solution and no further action, to suppress the effect of uncertainties, is expected. However, it is acknowledged that enhanced robustness comes at the expense of peak performances. In this paper, active robust optimization is presented as a new robust optimization approach. It considers products that are able to adapt to environmental changes. The enhanced robustness of these solutions is attained by adaptation, which reduces the loss in performance due to environmental changes. A new optimization problem named active robust optimization problem is formulated. The problem amalgamates robust optimization with dynamic optimization to evaluate the performance of a candidate solution, while considering possible environmental conditions. The adaptation's influence on the solution's performance and cost is considered as well. Hence, the problem is formulated as a multiobjective problem that simultaneously aims at low costs and high performance. Since these goals are commonly in conflict, the solution is a set of optimal adaptive solutions. An evolutionary algorithm is proposed in order to evolve this set. An example of optimizing an adaptive optical table is provided. It is shown that an adaptive product, which is an outcome of the suggested approach, may be superior to an equivalent product that is not adaptive.Many real world optimization problems involve uncertainties. A solution for such a problem is expected to be robust to these uncertainties. Commonly, robustness is attained by choosing the solution's parameters such that the solution's performance is less influenced by negative effects of the uncertain parameters' variations. This robustness may be viewed as a passive robustness, because once the solution's parameters are chosen, the robustness is inherent in the solution and no further action, to suppress the effect of uncertainties, is expected. However, it is acknowledged that enhanced robustness comes at the expense of peak performances. In this paper, active robust optimization is presented as a new robust optimization approach. It considers products that are able to adapt to environmental changes. The enhanced robustness of these solutions is attained by adaptation, which reduces the loss in performance due to environmental changes. A new optimization problem named active robust optimization problem is formulated. The problem amalgamates robust optimization with dynamic optimization to evaluate the performance of a candidate solution, while considering possible environmental conditions. The adaptation's influence on the solution's performance and cost is considered as well. Hence, the problem is formulated as a multiobjective problem that simultaneously aims at low costs and high performance. Since these goals are commonly in conflict, the solution is a set of optimal adaptive solutions. An evolutionary algorithm is proposed in order to evolve this set. An example of optimizing an adaptive optical table is provided. It is shown that an adaptive product, which is an outcome of the suggested approach, may be superior to an equivalent product that is not adaptive.
Author Fleming, Peter J.
Avigad, Gideon
Purshouse, Robin C.
Salomon, Shaul
Author_xml – sequence: 1
  givenname: Shaul
  surname: Salomon
  fullname: Salomon, Shaul
  email: ssalomon1@sheffield.ac.uk
  organization: Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield, Sheffield, UK
– sequence: 2
  givenname: Gideon
  surname: Avigad
  fullname: Avigad, Gideon
  organization: Dept. of Mech. Eng., ORT Braude Coll. of Eng., Karmiel, Israel
– sequence: 3
  givenname: Peter J.
  surname: Fleming
  fullname: Fleming, Peter J.
  organization: Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield, Sheffield, UK
– sequence: 4
  givenname: Robin C.
  surname: Purshouse
  fullname: Purshouse, Robin C.
  organization: Dept. of Autom. Control & Syst. Eng., Univ. of Sheffield, Sheffield, UK
BackLink https://www.ncbi.nlm.nih.gov/pubmed/25330479$$D View this record in MEDLINE/PubMed
BookMark eNqNkUtrGzEURkVJaZ4_oATCQDfZ2NVjJF1155j0RSAQkkVXQqPRJAoeyZU0gfbXV66dLLIo1Ubi6pwL936HaC_E4BB6T_CcEKw-3i5_XMwpJu2cMty2kr9BB5QImFEq-d7LW8h9dJLzI64HaknBO7RPOauOVAfo-8IW_-Sam9hNuTTX6-JH_9sUH8On5jI8mGB9uN99B5dzU2JzF6xLxfhQiSefYhhdKPkYvR3MKruT3X2E7j5f3i6_zq6uv3xbLq5mtuW0zIQA4UzPCIGuZ9CzAQwQbhUMhBpJJBOdGSRVXasMFw4sAB3koHoDjElgR-h823ed4s_J5aJHn61brUxwccqaCKGAkxbEf6CEUy4B44p-eIU-ximFOsiGailXSqhKne2oqRtdr9fJjyb90s8LrYDcAjbFnJMbtPXl7zpLMn6lCdab9PQmPb1JT-_SqyZ5ZT43_5dzunW8c-6FF7LFUin2BwYNos0
CODEN ITCEB8
CitedBy_id crossref_primary_10_1109_TCYB_2015_2415732
crossref_primary_10_1049_iet_stg_2018_0209
crossref_primary_10_1016_j_paerosci_2019_05_003
crossref_primary_10_1080_00207543_2024_2445710
crossref_primary_10_1016_j_arabjc_2022_104257
crossref_primary_10_1016_j_knosys_2020_105857
crossref_primary_10_1109_TCYB_2015_2510698
crossref_primary_10_1016_j_swevo_2021_100867
crossref_primary_10_1109_TCYB_2019_2937565
crossref_primary_10_1016_j_swevo_2019_03_015
crossref_primary_10_1109_TEVC_2021_3092343
crossref_primary_10_1109_TCST_2019_2908938
crossref_primary_10_3390_en9040278
crossref_primary_10_1109_TVT_2023_3342421
crossref_primary_10_1016_j_ast_2021_107283
crossref_primary_10_1080_0305215X_2015_1031659
crossref_primary_10_1155_2018_2517987
crossref_primary_10_1016_j_apm_2022_08_009
crossref_primary_10_1109_TCYB_2019_2895305
crossref_primary_10_1007_s12065_021_00672_0
crossref_primary_10_1016_j_asoc_2015_04_003
crossref_primary_10_1109_TCYB_2021_3049635
crossref_primary_10_1177_1687814019834131
crossref_primary_10_1007_s40171_025_00458_8
crossref_primary_10_1109_TAP_2019_2891661
crossref_primary_10_1051_ro_2025061
crossref_primary_10_3390_su16010292
crossref_primary_10_5004_dwt_2017_20381
Cites_doi 10.2307/1268522
10.1016/S0007-8506(07)60028-6
10.1287/moor.1110.0482
10.1007/978-3-642-31519-0_9
10.1109/TEVC.2005.859465
10.1007/978-3-642-37140-0_6
10.1109/TEVC.2005.846356
10.1016/S0007-8506(07)63232-6
10.1016/j.asoc.2009.11.009
10.1023/B:OPTE.0000048538.35456.45
10.1007/s10107-003-0454-y
10.1016/j.jmsy.2011.04.003
10.1007/s00500-010-0681-0
10.1080/03052150903271959
ContentType Journal Article
Copyright Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Nov 2014
Copyright_xml – notice: Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) Nov 2014
DBID 97E
RIA
RIE
AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
7SC
7SP
7TB
8FD
F28
FR3
H8D
JQ2
L7M
L~C
L~D
7X8
DOI 10.1109/TCYB.2014.2304475
DatabaseName IEEE All-Society Periodicals Package (ASPP) 2005–Present
IEEE All-Society Periodicals Package (ASPP) 1998–Present
IEEE Electronic Library (IEL)
CrossRef
Medline
MEDLINE
MEDLINE (Ovid)
MEDLINE
MEDLINE
PubMed
Computer and Information Systems Abstracts
Electronics & Communications Abstracts
Mechanical & Transportation Engineering Abstracts
Technology Research Database
ANTE: Abstracts in New Technology & Engineering
Engineering Research Database
Aerospace Database
ProQuest Computer Science Collection
Advanced Technologies Database with Aerospace
Computer and Information Systems Abstracts – Academic
Computer and Information Systems Abstracts Professional
MEDLINE - Academic
DatabaseTitle CrossRef
MEDLINE
Medline Complete
MEDLINE with Full Text
PubMed
MEDLINE (Ovid)
Aerospace Database
Technology Research Database
Computer and Information Systems Abstracts – Academic
Mechanical & Transportation Engineering Abstracts
Electronics & Communications Abstracts
ProQuest Computer Science Collection
Computer and Information Systems Abstracts
Engineering Research Database
Advanced Technologies Database with Aerospace
ANTE: Abstracts in New Technology & Engineering
Computer and Information Systems Abstracts Professional
MEDLINE - Academic
DatabaseTitleList Aerospace Database
MEDLINE
MEDLINE - Academic

Aerospace Database
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
– sequence: 2
  dbid: RIE
  name: IEEE Electronic Library (IEL)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
– sequence: 3
  dbid: 7X8
  name: MEDLINE - Academic
  url: https://search.proquest.com/medline
  sourceTypes: Aggregation Database
DeliveryMethod fulltext_linktorsrc
Discipline Sciences (General)
EISSN 2168-2275
EndPage 2231
ExternalDocumentID 3466222321
25330479
10_1109_TCYB_2014_2304475
6740799
Genre orig-research
Research Support, Non-U.S. Gov't
Journal Article
GrantInformation_xml – fundername: Marie Curie International Research Staff Exchange Scheme Fellowship within the 7th European Community Framework Programme
GroupedDBID 0R~
4.4
6IK
97E
AAJGR
AARMG
AASAJ
AAWTH
ABAZT
ABQJQ
ABVLG
ACIWK
AENEX
AGQYO
AGSQL
AHBIQ
AKJIK
AKQYR
ALMA_UNASSIGNED_HOLDINGS
ATWAV
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
EBS
EJD
HZ~
IFIPE
IPLJI
JAVBF
M43
O9-
OCL
PQQKQ
RIA
RIE
RNS
AAYXX
CITATION
CGR
CUY
CVF
ECM
EIF
NPM
RIG
7SC
7SP
7TB
8FD
F28
FR3
H8D
JQ2
L7M
L~C
L~D
7X8
ID FETCH-LOGICAL-c452t-6686ead3118bd38d3f8a815c98f12a71736baf729b49a56e8c882f7f9da833783
IEDL.DBID RIE
ISICitedReferencesCount 32
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000343319700020&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 2168-2267
2168-2275
IngestDate Mon Sep 29 05:43:21 EDT 2025
Sun Sep 28 03:16:13 EDT 2025
Sun Jun 29 16:00:22 EDT 2025
Mon Jul 21 06:07:08 EDT 2025
Sat Nov 29 06:48:28 EST 2025
Tue Nov 18 22:28:57 EST 2025
Tue Aug 26 16:49:28 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 11
Keywords multiobjective optimization
Adaptive design
dynamic optimization
evolutionary algorithms
robust optimization
Language English
License https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c452t-6686ead3118bd38d3f8a815c98f12a71736baf729b49a56e8c882f7f9da833783
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
PMID 25330479
PQID 1614259969
PQPubID 85422
PageCount 11
ParticipantIDs pubmed_primary_25330479
crossref_citationtrail_10_1109_TCYB_2014_2304475
proquest_miscellaneous_1615257800
crossref_primary_10_1109_TCYB_2014_2304475
proquest_miscellaneous_1669851486
ieee_primary_6740799
proquest_journals_1614259969
PublicationCentury 2000
PublicationDate 2014-11-01
PublicationDateYYYYMMDD 2014-11-01
PublicationDate_xml – month: 11
  year: 2014
  text: 2014-11-01
  day: 01
PublicationDecade 2010
PublicationPlace United States
PublicationPlace_xml – name: United States
– name: Piscataway
PublicationTitle IEEE transactions on cybernetics
PublicationTitleAbbrev TCYB
PublicationTitleAlternate IEEE Trans Cybern
PublicationYear 2014
Publisher IEEE
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher_xml – name: IEEE
– name: The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
References ref13
ref15
ref14
ref11
ref10
ref1
ref17
ref16
branke (ref3) 2008; lncs 5199
phadke (ref2) 1995
ref8
ref7
ref9
ref4
ref6
ref5
(ref12) 2013
References_xml – ident: ref11
  doi: 10.2307/1268522
– ident: ref6
  doi: 10.1016/S0007-8506(07)60028-6
– year: 2013
  ident: ref12
  publication-title: Vibration control Identifying and controlling vibrations in the workplace
– ident: ref9
  doi: 10.1287/moor.1110.0482
– ident: ref13
  doi: 10.1007/978-3-642-31519-0_9
– ident: ref1
  doi: 10.1109/TEVC.2005.859465
– ident: ref17
  doi: 10.1007/978-3-642-37140-0_6
– year: 1995
  ident: ref2
  publication-title: Quality Engineering Using Robust Design
– ident: ref16
  doi: 10.1109/TEVC.2005.846356
– ident: ref5
  doi: 10.1016/S0007-8506(07)63232-6
– ident: ref15
  doi: 10.1016/j.asoc.2009.11.009
– ident: ref14
  doi: 10.1023/B:OPTE.0000048538.35456.45
– ident: ref8
  doi: 10.1007/s10107-003-0454-y
– ident: ref7
  doi: 10.1016/j.jmsy.2011.04.003
– volume: lncs 5199
  start-page: 144
  year: 2008
  ident: ref3
  article-title: New approaches to coevolutionary worst-case optimization
  publication-title: Proc PPSN X
– ident: ref4
  doi: 10.1007/s00500-010-0681-0
– ident: ref10
  doi: 10.1080/03052150903271959
SSID ssj0000816898
Score 2.1995175
Snippet Many real world optimization problems involve uncertainties. A solution for such a problem is expected to be robust to these uncertainties. Commonly,...
SourceID proquest
pubmed
crossref
ieee
SourceType Aggregation Database
Index Database
Enrichment Source
Publisher
StartPage 2221
SubjectTerms Adaptation
Adaptive design
Algorithms
Artificial Intelligence
Brightness
Computer Simulation
Cybernetics
Decision Support Techniques
dynamic optimization
Dynamic tests
Ecosystem
Equivalence
evolutionary algorithms
Expenses
Feedback
Linear programming
Models, Statistical
multiobjective optimization
Nonlinear programming
Optimization
Pareto optimization
Pattern Recognition, Automated - methods
robust optimization
Robustness
Tables (data)
Uncertainty
Vectors
Title Active Robust Optimization: Enhancing Robustness to Uncertain Environments
URI https://ieeexplore.ieee.org/document/6740799
https://www.ncbi.nlm.nih.gov/pubmed/25330479
https://www.proquest.com/docview/1614259969
https://www.proquest.com/docview/1615257800
https://www.proquest.com/docview/1669851486
Volume 44
WOSCitedRecordID wos000343319700020&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVIEE
  databaseName: IEEE Electronic Library (IEL)
  customDbUrl:
  eissn: 2168-2275
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0000816898
  issn: 2168-2267
  databaseCode: RIE
  dateStart: 20130101
  isFulltext: true
  titleUrlDefault: https://ieeexplore.ieee.org/
  providerName: IEEE
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3NS-wwEB9UPLyLuvrU1VXywIPvYbW7SdPEm8qKPERFFNZT6Ueigrbidv37nUmzxYMK3gqZtiEzmfwm8wWwk5pMcsNFIIXMAqFzGejIoOGa95UO7cCGTXX98_jiQo1G-moG9tpcGGOMCz4z-_TofPlFlU_oquxAxmh-aD0Ls3Esm1yt9j7FNZBwrW8H-BAgqoi9E7Mf6oObk7tjiuMSFPhMNe6oCDAFVgqK4fpwIrkWK1-jTXfqnC7-bL5LsODRJTtqxKEDM6Zcho7fv2O264tM_12B_0dO0bHrKpuMa3aJmuPZp2QesmH5QGU4yns_TOqQ1RW7xa-4CAI2_JAf9xtuT4c3J2eB76sQ5CIa1IGUSqIAcbQtsoKrgluVqn6Ua2X7g5Tc8jJLLaLuTOg0kkblCMNtbHWRKs5jxVdhrqxKsw5MRtpKTR51XojMRopbRJyos9AuC1NtuxBO1zbJfdFx6n3xlDjjI9QJcSYhziSeM134177y0lTc-I54hZa9JfQr3oXelIGJ35PjBLEtKii073D4TzuMu4lcJGlpqomjofKwiKK_o5EacapQsgtrjXC0_5_K1Mbn89qEXzT7JpexB3P168RswXz-Vj-OX7dRrEdq24n1OzzS7r0
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT9wwEB4BRaIXWh4tS2lrJA6AmpKNHcfujaJFPJalqhaJnqI87BYJEsRm-f2dcbwRh4LUmyU7ieMZj7_xvAB2MpNLbrgIpJB5IHQhAx0bVFyLvtKhjWzYZtcfJqORur7WP-bgSxcLY4xxzmfmKzWdLb-siyldlR3IBNUPrefhVSxEFLbRWt2Niish4YrfRtgIEFck3ozZD_XB-OjXd_LkEuT6TFnuKA0wuVYK8uJ6cia5IivP40137hy_-b8Zv4Vljy_ZYcsQKzBnqlVY8Tt4wnZ9mum9NTg7dKKO_azz6aRhlyg77nxQ5jc2qP5QIo7qt-8mgciaml3hW5wPARs8iZBbh6vjwfjoJPCVFYJCxFETSKkkshBH7SIvuSq5VZnqx4VWth9lZJiXeWYRd-dCZ7E0qkAgbhOry0xxnij-DhaqujIbwGSsrdRkU-elyG2suEXMiVILNbMw07YH4Wxt08KnHafqF7epUz9CnRJlUqJM6inTg_3ukfs258ZLg9do2buBfsV7sDUjYOp35SRFdIsiCjU87N7uunE_kZEkq0w9dWMoQSzi6JfGSI1IVSjZg_ctc3Tfn_HU5r_n9RmWTsYXw3R4Ojr_AK_pT9rIxi1YaB6m5iMsFo_NzeThk2Puvy5D8Rw
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%3Ajournal&rft.genre=article&rft.atitle=Active+Robust+Optimization%3A+Enhancing+Robustness+to+Uncertain+Environments&rft.jtitle=IEEE+transactions+on+cybernetics&rft.au=Salomon%2C+Shaul&rft.au=Avigad%2C+Gideon&rft.au=Fleming%2C+Peter+J.&rft.au=Purshouse%2C+Robin+C.&rft.date=2014-11-01&rft.pub=IEEE&rft.issn=2168-2267&rft.volume=44&rft.issue=11&rft.spage=2221&rft.epage=2231&rft_id=info:doi/10.1109%2FTCYB.2014.2304475&rft_id=info%3Apmid%2F25330479&rft.externalDocID=6740799
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=2168-2267&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=2168-2267&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=2168-2267&client=summon