An improved Gaussian distribution based quantum-behaved particle swarm optimization algorithm for engineering shape design problems

In this article, an improved Gaussian distribution based quantum-behaved particle swarm optimization (IG-QPSO) algorithm is proposed to solve engineering shape design problems with multiple constraints. In this algorithm, the Gaussian distribution is employed to generate the sequence of random numbe...

Full description

Saved in:
Bibliographic Details
Published in:Engineering optimization Vol. 54; no. 5; pp. 743 - 769
Main Authors: Chen, Qidong, Sun, Jun, Palade, Vasile, Wu, Xiaojun, Shi, Xiaoqian
Format: Journal Article
Language:English
Published: Taylor & Francis 04.05.2022
Subjects:
Engineering shape design problems
Gaussian distribution
multiple constraints
quantum-behaved optimization algorithm
weighted mean best position
ISSN:0305-215X, 1029-0273
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract In this article, an improved Gaussian distribution based quantum-behaved particle swarm optimization (IG-QPSO) algorithm is proposed to solve engineering shape design problems with multiple constraints. In this algorithm, the Gaussian distribution is employed to generate the sequence of random numbers in the QPSO algorithm. By decreasing the variance of the Gaussian distribution linearly, the algorithm is able not only to maintain its global search ability during the early search stages, but can also obtain gradually enhanced local search ability in the later search stages. Additionally, a weighted mean best position in the IG-QPSO is employed to achieve a good balance between local search and global search. The proposed algorithm and some other well-known PSO variants are tested on ten standard benchmark functions and six well-studied engineering shape design problems. Experimental results show that the IG-QPSO algorithm can optimize these problems effectively in terms of precision and robustness compared to its competitors.
AbstractList In this article, an improved Gaussian distribution based quantum-behaved particle swarm optimization (IG-QPSO) algorithm is proposed to solve engineering shape design problems with multiple constraints. In this algorithm, the Gaussian distribution is employed to generate the sequence of random numbers in the QPSO algorithm. By decreasing the variance of the Gaussian distribution linearly, the algorithm is able not only to maintain its global search ability during the early search stages, but can also obtain gradually enhanced local search ability in the later search stages. Additionally, a weighted mean best position in the IG-QPSO is employed to achieve a good balance between local search and global search. The proposed algorithm and some other well-known PSO variants are tested on ten standard benchmark functions and six well-studied engineering shape design problems. Experimental results show that the IG-QPSO algorithm can optimize these problems effectively in terms of precision and robustness compared to its competitors.
Author Sun, Jun
Shi, Xiaoqian
Palade, Vasile
Chen, Qidong
Wu, Xiaojun
Author_xml – sequence: 1
  givenname: Qidong
  orcidid: 0000-0001-9434-4979
  surname: Chen
  fullname: Chen, Qidong
  organization: Jiangnan University
– sequence: 2
  givenname: Jun
  orcidid: 0000-0002-9824-4294
  surname: Sun
  fullname: Sun, Jun
  email: junsun@jiangnan.edu.cn
  organization: Jiangnan University
– sequence: 3
  givenname: Vasile
  orcidid: 0000-0002-6768-8394
  surname: Palade
  fullname: Palade, Vasile
  organization: Coventry University
– sequence: 4
  givenname: Xiaojun
  orcidid: 0000-0002-0310-5778
  surname: Wu
  fullname: Wu, Xiaojun
  organization: Jiangnan University
– sequence: 5
  givenname: Xiaoqian
  orcidid: 0000-0002-9974-4856
  surname: Shi
  fullname: Shi, Xiaoqian
  organization: Taihu School
BookMark eNqFkE1P3DAQhq2KSizQn4DkP5DtOI6TjXopQi2thMQFJG7RxB-7UyV2sL0guPaPN-HjwqGc5jDv847mOWIHPnjL2KmAtYANfAUJqhTqdl1CKdaiBRCq-sRWAsq2gLKRB2y1ZIoldMiOUvozRyTAZsX-nnlO4xTDvTX8AvcpEXpuKOVI_T5T8LzHNO_u9ujzfix6u8MlO2HMpAfL0wPGkYcp00hP-EzgsA2R8m7kLkRu_Za8tZH8lqcdTpYbm2jr-Xy1H-yYTthnh0OyX17nMbv5-eP6_FdxeXXx-_zsstBSQC5aV1Z141yLDZa96AUKp1XTgnVG6dopU7VGmho2qGsrGt1L2Sio0BqolK3lMVMvvTqGlKJ13RRpxPjYCegWk92byW4x2b2anLlv7zhN-fnTHJGGD-nvLzT52caIDyEOpsv4OIToInpNqZP_r_gHIBqTWw
CitedBy_id crossref_primary_10_1007_s10489_022_03900_x
crossref_primary_10_1088_1402_4896_ad91ef
crossref_primary_10_3390_app12031518
crossref_primary_10_3233_IDA_226753
crossref_primary_10_1016_j_probengmech_2023_103494
crossref_primary_10_1080_0305215X_2023_2260992
crossref_primary_10_1016_j_future_2024_04_008
crossref_primary_10_3390_pr10112312
crossref_primary_10_1080_09500340_2024_2359072
Cites_doi 10.1016/j.asoc.2015.10.048
10.1109/TEVC.2003.814902
10.1002/9780470640425
10.1115/1.2912596
10.1016/S0166-3615(99)00046-9
10.1109/ICCAE.2010.5451501
10.1007/s10994-015-5522-z
10.1109/TCYB.2015.2475174
10.1109/CEC.2002.1004459
10.1162/EVCO_a_00049
10.1145/1143997.1144266
10.1109/TEVC.2004.826074
10.1016/j.ins.2019.08.054
10.1080/03052150108940941
10.1016/0094-114X(73)90018-9
10.1016/j.amc.2006.07.134
10.1115/1.3438995
10.1109/TCYB.2014.2322602
10.1016/j.eswa.2009.06.044
10.1109/ICNN.1995.488968
10.1016/j.ins.2014.09.053
10.1016/j.engappai.2006.03.003
10.1016/j.amc.2015.06.062
ContentType Journal Article
Copyright 2021 Informa UK Limited, trading as Taylor & Francis Group 2021
Copyright_xml – notice: 2021 Informa UK Limited, trading as Taylor & Francis Group 2021
DBID AAYXX
CITATION
DOI 10.1080/0305215X.2021.1900154
DatabaseName CrossRef
DatabaseTitle CrossRef
DatabaseTitleList
DeliveryMethod fulltext_linktorsrc
Discipline Engineering
EISSN 1029-0273
EndPage 769
ExternalDocumentID 10_1080_0305215X_2021_1900154
1900154
Genre Research Article
GrantInformation_xml – fundername: National Natural Science Foundation of China
  grantid: 61672263,61672265,61673194
GroupedDBID -~X
.7F
.QJ
0BK
0R~
29G
2DF
30N
4.4
5GY
5VS
AAENE
AAGDL
AAHIA
AAJMT
AALDU
AAMIU
AAPUL
AAQRR
ABCCY
ABFIM
ABHAV
ABJNI
ABLIJ
ABPAQ
ABPEM
ABTAI
ABXUL
ABXYU
ACGEJ
ACGFS
ACGOD
ACIWK
ACTIO
ADCVX
ADGTB
ADXPE
AEISY
AENEX
AEOZL
AEPSL
AEYOC
AFKVX
AFRVT
AGDLA
AGMYJ
AHDZW
AIJEM
AIYEW
AJWEG
AKBVH
AKOOK
ALMA_UNASSIGNED_HOLDINGS
ALQZU
AQRUH
AQTUD
AVBZW
AWYRJ
BLEHA
CCCUG
CE4
CS3
DGEBU
DKSSO
EBS
E~A
E~B
GTTXZ
H13
HF~
HZ~
H~P
IPNFZ
J.P
KYCEM
LJTGL
M4Z
NA5
NX~
O9-
P2P
PQQKQ
RIG
RNANH
ROSJB
RTWRZ
S-T
SNACF
TASJS
TBQAZ
TDBHL
TEN
TFL
TFT
TFW
TN5
TNC
TTHFI
TUROJ
TWF
UT5
UU3
ZGOLN
~S~
AAYXX
CITATION
ID FETCH-LOGICAL-c310t-9f2467ff9a7a2b1b1a1fc5790efd5c6f5d49d3d608ac6e17cb337504aed045e63
IEDL.DBID TFW
ISICitedReferencesCount 11
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000632328400001&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
ISSN 0305-215X
IngestDate Tue Nov 18 21:40:05 EST 2025
Sat Nov 29 06:22:50 EST 2025
Mon Oct 20 23:47:03 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 5
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c310t-9f2467ff9a7a2b1b1a1fc5790efd5c6f5d49d3d608ac6e17cb337504aed045e63
ORCID 0000-0002-9824-4294
0000-0001-9434-4979
0000-0002-0310-5778
0000-0002-9974-4856
0000-0002-6768-8394
PageCount 27
ParticipantIDs crossref_primary_10_1080_0305215X_2021_1900154
crossref_citationtrail_10_1080_0305215X_2021_1900154
informaworld_taylorfrancis_310_1080_0305215X_2021_1900154
PublicationCentury 2000
PublicationDate 2022-05-04
PublicationDateYYYYMMDD 2022-05-04
PublicationDate_xml – month: 05
  year: 2022
  text: 2022-05-04
  day: 04
PublicationDecade 2020
PublicationTitle Engineering optimization
PublicationYear 2022
Publisher Taylor & Francis
Publisher_xml – name: Taylor & Francis
References CIT0010
CIT0012
CIT0011
CIT0014
CIT0013
CIT0016
CIT0015
CIT0018
CIT0017
CIT0019
CIT0021
CIT0020
CIT0001
CIT0023
CIT0003
CIT0025
CIT0002
CIT0024
CIT0005
CIT0004
CIT0007
CIT0006
CIT0009
Wang Yong (CIT0022) 2018
CIT0008
References_xml – ident: CIT0008
  doi: 10.1016/j.asoc.2015.10.048
– ident: CIT0016
  doi: 10.1109/TEVC.2003.814902
– ident: CIT0023
  doi: 10.1002/9780470640425
– ident: CIT0018
  doi: 10.1115/1.2912596
– ident: CIT0001
  doi: 10.1016/S0166-3615(99)00046-9
– ident: CIT0012
  doi: 10.1109/ICCAE.2010.5451501
– ident: CIT0020
  doi: 10.1007/s10994-015-5522-z
– ident: CIT0007
  doi: 10.1109/TCYB.2015.2475174
– ident: CIT0013
  doi: 10.1109/CEC.2002.1004459
– ident: CIT0019
  doi: 10.1162/EVCO_a_00049
– ident: CIT0003
  doi: 10.1145/1143997.1144266
– ident: CIT0014
  doi: 10.1109/TEVC.2004.826074
– ident: CIT0025
  doi: 10.1016/j.ins.2019.08.054
– ident: CIT0017
  doi: 10.1080/03052150108940941
– year: 2018
  ident: CIT0022
  publication-title: IEEE Transactions on Cybernetics
– ident: CIT0006
  doi: 10.1016/0094-114X(73)90018-9
– ident: CIT0010
  doi: 10.1016/j.amc.2006.07.134
– ident: CIT0015
  doi: 10.1115/1.3438995
– ident: CIT0002
  doi: 10.1109/TCYB.2014.2322602
– ident: CIT0004
  doi: 10.1016/j.eswa.2009.06.044
– ident: CIT0024
– ident: CIT0011
  doi: 10.1109/ICNN.1995.488968
– ident: CIT0021
  doi: 10.1016/j.ins.2014.09.053
– ident: CIT0009
  doi: 10.1016/j.engappai.2006.03.003
– ident: CIT0005
  doi: 10.1016/j.amc.2015.06.062
SSID ssj0013008
Score 2.3597975
Snippet In this article, an improved Gaussian distribution based quantum-behaved particle swarm optimization (IG-QPSO) algorithm is proposed to solve engineering shape...
SourceID crossref
informaworld
SourceType Enrichment Source
Index Database
Publisher
StartPage 743
SubjectTerms Engineering shape design problems
Gaussian distribution
multiple constraints
quantum-behaved optimization algorithm
weighted mean best position
Title An improved Gaussian distribution based quantum-behaved particle swarm optimization algorithm for engineering shape design problems
URI https://www.tandfonline.com/doi/abs/10.1080/0305215X.2021.1900154
Volume 54
WOSCitedRecordID wos000632328400001&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: PRVAWR
  databaseName: Taylor & Francis Online Journals
  customDbUrl:
  eissn: 1029-0273
  dateEnd: 99991231
  omitProxy: false
  ssIdentifier: ssj0013008
  issn: 0305-215X
  databaseCode: TFW
  dateStart: 19740101
  isFulltext: true
  titleUrlDefault: https://www.tandfonline.com
  providerName: Taylor & Francis
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELZQxQADb0R5yQNrSh7Ow2OFKEwVQxHdIj9ppaYtecAP4I_jSxxaBmCAMcM5ie98d7bvvg-hK0K171Jmdifc7HQI18JJeKQd5sdwj0U55w3ZRDwcJuMxfbDVhIUtq4Q9tG6AImpfDYub8aKtiLsGGzWRamx2d77XMxEN8gDjhU3oh6U5Gjyt7hHcmpMOJBwQaXt4vhvlS3T6gl26FnUGu__wvXtox6acuN_YyD7aUPMDtL0GRHiI3vtzPK2PF5TEd6wqoLUSSwDVtXxYGMKdxC-V0USVOXV3v3leWsvDxRvLM7wwDiiznZ2YzZ4X-bScZNj8H1ar9-FiwpYKy7p4BFtKm-IIPQ5uRzf3jqVncITJCUvHaNl4Wa0pi5nPPe4xT4swpq7SMhSRDiWhMpCRmzARKS8WPAgATJ4pafJIFQXHqDNfzNUJwlSJhKoQCHM0ISxMfKK5GdVlVNDEFV1EWrWkwmKXA4XGLPVaiFM7xynMcWrnuIt6n2LLBrzjNwG6rvO0rE9NdENxkgY_yp7-QfYMbfnQVgGFlOQcdcq8UhdoU7yW0yK_rE36A80-9QY
linkProvider Taylor & Francis
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV1LU8IwEM4oOqMefDviMwevxb7bHBlHxBE54citk6cwQwFp0R_gHzfbBoGDetBjD5u02W12N9n9PoSufKJcm1CdnTCd6fhMcStmobKoG8E9FmGMlWQTUbsdd7tksRcGyiohh1YlUESxV8PPDYfRs5K4azBS7aq6Or1znZp2aRAIrKK1QPtawM_vNJ7nNwl2wUoHIhbIzLp4vhtmyT8toZcu-J3Gzn-88S7aNlEnrpdmsodW5HAfbS1gER6gj_oQ94sTBinwHZ1m0F2JBeDqGkosDB5P4NepVsY0tYoGf_08NsaHs3c6SfFI70Gpae7EdPAymvTzXor1B2I5nw9nPTqWWBT1I9iw2mSH6Klx27lpWoahweI6LMwtrWi90SpFaERd5jCHOooHEbGlEgEPVSB8IjwR2jHloXQizjwP8OSpFDqUlKF3hCrD0VAeI0wkj4kMgDNH-T4NYtdXTI9qU8JJbPMq8md6SbiBLwcWjUHizFBOzRonsMaJWeMqqn2JjUv8jt8EyKLSk7w4OFEly0ni_Sh78gfZS7TR7Dy2ktZ9--EUbbrQZQF1lf4ZquSTqTxH6_wt72eTi8K-PwFQZfkw
linkToPdf http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwrV07T8MwELagIAQDb0R5emBNyTvxWAEFBKo6FNEt8pNWatPSpPAD-OP4Epe2AzDAmOHsxHfx3dl334fQhU-UaxOqsxOmMx2fKW7FLFQWdSO4xyKMsZJsImo2406HtEw1YWbKKiGHViVQRLFXw889EmpaEXcJNqo9VUdnd65T0x4N4oBltKJD5xCMvN14nl0k2AUpHYhYIDNt4vlumAX3tABeOud2Glv_8MLbaNPEnLheGskOWpLpLtqYQyLcQx_1FPeK8wUp8C2dZNBbiQWg6hpCLAz-TuDXiVbFZGAV7f36eWRMD2fvdDzAQ70DDUxrJ6b9l-G4l3cHWH8flrP5cNalI4lFUT2CDadNto-eGjftqzvL8DNYXAeFuaXVrLdZpQiNqMsc5lBH8SAitlQi4KEKhE-EJ0I7pjyUTsSZ5wGaPJVCB5Iy9A5QJR2m8hBhInlMZACMOcr3aRC7vmJ6VJsSTmKbV5E_VUvCDXg5cGj0E2eKcWrWOIE1TswaV1HtS2xUonf8JkDmdZ7kxbGJKjlOEu9H2aM_yJ6jtdZ1I3m8bz4co3UXWiygqNI_QZV8PJGnaJW_5b1sfFZY9ydnK_fi
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=An+improved+Gaussian+distribution+based+quantum-behaved+particle+swarm+optimization+algorithm+for+engineering+shape+design+problems&rft.jtitle=Engineering+optimization&rft.au=Chen%2C+Qidong&rft.au=Sun%2C+Jun&rft.au=Palade%2C+Vasile&rft.au=Wu%2C+Xiaojun&rft.date=2022-05-04&rft.issn=0305-215X&rft.eissn=1029-0273&rft.volume=54&rft.issue=5&rft.spage=743&rft.epage=769&rft_id=info:doi/10.1080%2F0305215X.2021.1900154&rft.externalDBID=n%2Fa&rft.externalDocID=10_1080_0305215X_2021_1900154
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0305-215X&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0305-215X&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0305-215X&client=summon