Evolutionary Dynamic Constrained Multiobjective Optimization: Test Suite and Algorithm
Dynamic constrained multiobjective optimization problems (DCMOPs) abound in real-world applications and gain increasing attention in the evolutionary computation community. To evaluate the capability of an algorithm in solving dynamic constrained multiobjective optimization problems (DCMOPs), artifi...
Uložené v:
| Vydané v: | IEEE transactions on evolutionary computation Ročník 28; číslo 5; s. 1381 - 1395 |
|---|---|
| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
IEEE
01.10.2024
|
| Predmet: | |
| ISSN: | 1089-778X, 1941-0026 |
| On-line prístup: | Získať plný text |
| Tagy: |
Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
|
| Abstract | Dynamic constrained multiobjective optimization problems (DCMOPs) abound in real-world applications and gain increasing attention in the evolutionary computation community. To evaluate the capability of an algorithm in solving dynamic constrained multiobjective optimization problems (DCMOPs), artificial test problems play a fundamental role. Nevertheless, some characteristics of real-world scenarios are not fully considered in the previous test suites, such as time-varying size, location, and shape of feasible regions, the controllable change severity, as well as small feasible regions. Therefore, we develop the generators of objective functions and constraints to facilitate the systematic design of DCMOPs, and then a novel test suite consisting of nine benchmarks, termed as DCP, is put forward. To solve these problems, a dynamic constrained multiobjective evolutionary algorithm (DCMOEA) with a two-stage diversity compensation strategy (TDCEA) is proposed. Some initial individuals are randomly generated to replace historical ones in the first stage, improving the global diversity. In the second stage, the increment between center points of Pareto sets in the past two environments is calculated and employed to adaptively disturb solutions, forming an initial population with good diversity for the new environment. Intensive experiments show that the proposed test problems enable a good understanding of strengths and weaknesses of algorithms, and TDCEA outperforms other state-of-the-art comparative ones, achieving promising performance in tackling DCMOPs. |
|---|---|
| AbstractList | Dynamic constrained multiobjective optimization problems (DCMOPs) abound in real-world applications and gain increasing attention in the evolutionary computation community. To evaluate the capability of an algorithm in solving dynamic constrained multiobjective optimization problems (DCMOPs), artificial test problems play a fundamental role. Nevertheless, some characteristics of real-world scenarios are not fully considered in the previous test suites, such as time-varying size, location, and shape of feasible regions, the controllable change severity, as well as small feasible regions. Therefore, we develop the generators of objective functions and constraints to facilitate the systematic design of DCMOPs, and then a novel test suite consisting of nine benchmarks, termed as DCP, is put forward. To solve these problems, a dynamic constrained multiobjective evolutionary algorithm (DCMOEA) with a two-stage diversity compensation strategy (TDCEA) is proposed. Some initial individuals are randomly generated to replace historical ones in the first stage, improving the global diversity. In the second stage, the increment between center points of Pareto sets in the past two environments is calculated and employed to adaptively disturb solutions, forming an initial population with good diversity for the new environment. Intensive experiments show that the proposed test problems enable a good understanding of strengths and weaknesses of algorithms, and TDCEA outperforms other state-of-the-art comparative ones, achieving promising performance in tackling DCMOPs. |
| Author | Liang, Jing Yang, Shengxiang Guo, Yinan Chen, Guoyu Gong, Dunwei Wang, Yong |
| Author_xml | – sequence: 1 givenname: Guoyu orcidid: 0000-0002-9228-6370 surname: Chen fullname: Chen, Guoyu email: chenguoyumail@163.com organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China – sequence: 2 givenname: Yinan orcidid: 0000-0002-4276-5410 surname: Guo fullname: Guo, Yinan email: nanfly@126.com organization: School of Information and Control Engineering, China University of Mining and Technology, Xuzhou, China – sequence: 3 givenname: Yong orcidid: 0000-0001-7670-3958 surname: Wang fullname: Wang, Yong email: ywang@csu.edu.cn organization: School of Automation, Central South University, Changsha, China – sequence: 4 givenname: Jing orcidid: 0000-0003-0811-0223 surname: Liang fullname: Liang, Jing email: liangjing@zzu.edu.cn organization: School of Electrical Engineering, Zhengzhou University, Zhengzhou, China – sequence: 5 givenname: Dunwei orcidid: 0000-0003-2838-4301 surname: Gong fullname: Gong, Dunwei email: dwgong@vip.163.com organization: School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao, China – sequence: 6 givenname: Shengxiang orcidid: 0000-0001-7222-4917 surname: Yang fullname: Yang, Shengxiang email: syang@dmu.ac.uk organization: Institute of Artificial Intelligence, School of Computer Science and Informatics, De Montfort University, Leicester, U.K |
| BookMark | eNp9kMtKw0AUhgepYFt9AMHFvEDqXNKZjLsS6wUqXViLuzBXnZJMSjIt1Kc3sV2IC1fnwPm_w883AoNQBwvANUYTjJG4Xc3X-YQgQieUYsoycQaGWKQ4QYiwQbejTCScZ-8XYNS2G4RwOsViCNbzfV3uoq-DbA7w_hBk5TXM69DGRvpgDXzZld1ZbayOfm_hcht95b9kj9zBlW0jfN35aKEMBs7Kj7rx8bO6BOdOlq29Os0xeHuYr_KnZLF8fM5ni0QTxmKCHWJEmFRRqanixAjFp1xLLRlzmaWEGGakNg4rlNnM8alDKVVMpw5xpxwdA378q5u6bRvrCu3jT7e-fllgVPR6il5P0espTno6Ev8ht42vOgn_MjdHxltrf-VJyihG9BsD9HWo |
| CODEN | ITEVF5 |
| CitedBy_id | crossref_primary_10_1016_j_swevo_2025_102067 crossref_primary_10_1109_TETCI_2024_3515013 crossref_primary_10_3390_math12142285 crossref_primary_10_1016_j_swevo_2023_101461 crossref_primary_10_1016_j_eswa_2025_127792 crossref_primary_10_1016_j_eswa_2025_129325 crossref_primary_10_1016_j_eswa_2025_129304 |
| Cites_doi | 10.1109/TEVC.2021.3060012 10.1109/CEC.2009.4983012 10.1007/978-3-319-42978-6_2 10.1109/TMAG.2004.825006 10.1109/CEC.2017.7969433 10.1007/s00500-010-0674-z 10.1016/j.ins.2021.07.078 10.1109/TCYB.2021.3069814 10.1109/TCYB.2020.2989465 10.1016/j.asoc.2013.10.008 10.1631/jzus.C0910072 10.1109/TEVC.2007.902851 10.1109/TEVC.2004.831456 10.1109/CEC.2006.1688283 10.1109/TEVC.2009.2033582 10.1109/TEVC.2019.2951217 10.1007/978-3-642-21524-7_9 10.1016/j.swevo.2019.03.015 10.1109/4235.996017 10.1016/j.ins.2019.01.066 10.1109/TEVC.2007.913121 10.1007/s00158-008-0269-9 10.1007/978-3-319-31153-1_20 10.1109/TCYB.2019.2909806 10.1109/TCYB.2015.2490738 10.1016/j.ins.2013.03.002 10.3390/app8091673 10.1109/TEVC.2018.2855411 10.1109/TCYB.2015.2493239 10.1109/TEVC.2019.2958075 10.1109/TCYB.2020.3021138 10.1109/TCYB.2021.3112675 10.1109/TEVC.2019.2896967 10.1016/j.swevo.2011.02.002 10.1007/3-540-44719-9_20 10.1016/j.ins.2020.07.009 10.1109/TEVC.2016.2574621 10.1109/TCYB.2015.2510698 10.1109/TEM.2017.2766443 10.1016/j.asoc.2012.02.025 10.1109/4235.873238 10.1016/j.swevo.2021.100930 10.1109/TCYB.2017.2647742 10.1109/CEC.2010.5586396 10.1162/EVCO_a_00097 10.1145/2739480.2754708 10.3182/20120215-3-AT-3016.00066 10.1007/s00500-015-1820-4 10.1007/978-3-642-00619-7_7 10.1109/tevc.2023.3241762 10.1109/TEVC.2005.846356 10.1109/TEVC.2020.3004012 10.1109/tevc.2022.3222844 10.1109/TSMC.2022.3221466 10.1109/TEVC.2020.3004027 10.1016/j.ins.2022.05.050 10.1109/TEVC.2019.2925722 10.1109/TEVC.2008.2009032 10.1109/TSMC.2018.2858843 10.1109/TCYB.2020.3017017 10.1109/JSYST.2021.3061670 10.1109/TEVC.2011.2180533 10.1109/CEC.2006.1688406 10.1109/TCYB.2013.2245892 10.1109/HIS.2006.264943 10.1145/3524495 10.1109/TCYB.2014.2345478 10.1007/1-84628-137-7_6 10.1016/j.swevo.2017.10.005 10.1016/j.oceaneng.2017.12.049 |
| ContentType | Journal Article |
| DBID | 97E RIA RIE AAYXX CITATION |
| DOI | 10.1109/TEVC.2023.3313689 |
| DatabaseName | IEEE Xplore (IEEE) IEEE All-Society Periodicals Package (ASPP) 1998–Present IEEE Electronic Library (IEL) CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| Database_xml | – sequence: 1 dbid: RIE name: IEEE Electronic Library (IEL) url: https://ieeexplore.ieee.org/ sourceTypes: Publisher |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering Statistics Computer Science |
| EISSN | 1941-0026 |
| EndPage | 1395 |
| ExternalDocumentID | 10_1109_TEVC_2023_3313689 10246310 |
| Genre | orig-research |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China grantid: 61973305; 61573361 funderid: 10.13039/501100001809 – fundername: National Key Research and Development Program of China grantid: 2022YFB4703701 – fundername: Foundation of Key Laboratory of System Control and Information Processing, Ministry of Education, China grantid: Scip202203 – fundername: Higher Education Discipline Innovation Project; 111 Project grantid: B21014 funderid: 10.13039/501100013314 |
| GroupedDBID | -~X .DC 0R~ 29I 4.4 5GY 5VS 6IF 6IK 6IL 6IN 97E AAJGR AARMG AASAJ AAWTH ABAZT ABJNI ABQJQ ABVLG ACGFO ACGFS ACIWK ADZIZ AENEX AETIX AGQYO AGSQL AHBIQ AI. AIBXA AKJIK AKQYR ALLEH ALMA_UNASSIGNED_HOLDINGS ATWAV BEFXN BFFAM BGNUA BKEBE BPEOZ CHZPO CS3 EBS EJD HZ~ H~9 IEGSK IFIPE IFJZH IPLJI JAVBF LAI M43 O9- OCL P2P PQQKQ RIA RIE RIL RNS TN5 VH1 AAYXX CITATION |
| ID | FETCH-LOGICAL-c266t-1f0629d4b3ac3b72d9b757caca66f8e322d6dacdf1b08e8f75f043b6c4f07fbf3 |
| IEDL.DBID | RIE |
| ISICitedReferencesCount | 18 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=001328314100009&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1089-778X |
| IngestDate | Sat Nov 29 03:13:50 EST 2025 Tue Nov 18 22:18:26 EST 2025 Wed Aug 27 02:20:03 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 5 |
| Language | English |
| License | https://ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html https://doi.org/10.15223/policy-029 https://doi.org/10.15223/policy-037 |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c266t-1f0629d4b3ac3b72d9b757caca66f8e322d6dacdf1b08e8f75f043b6c4f07fbf3 |
| ORCID | 0000-0003-0811-0223 0000-0001-7670-3958 0000-0002-4276-5410 0000-0002-9228-6370 0000-0003-2838-4301 0000-0001-7222-4917 |
| PageCount | 15 |
| ParticipantIDs | ieee_primary_10246310 crossref_citationtrail_10_1109_TEVC_2023_3313689 crossref_primary_10_1109_TEVC_2023_3313689 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-10-01 |
| PublicationDateYYYYMMDD | 2024-10-01 |
| PublicationDate_xml | – month: 10 year: 2024 text: 2024-10-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | IEEE transactions on evolutionary computation |
| PublicationTitleAbbrev | TEVC |
| PublicationYear | 2024 |
| Publisher | IEEE |
| Publisher_xml | – name: IEEE |
| References | ref13 ref57 ref12 ref56 ref15 ref59 ref14 ref58 ref53 ref52 ref11 ref55 ref10 ref54 ref17 ref16 ref19 ref18 ref51 ref50 ref46 ref45 ref48 ref47 ref42 ref41 ref44 ref43 ref49 ref8 ref7 ref9 ref4 ref3 ref6 ref5 ref40 ref35 ref34 ref37 ref36 ref31 ref30 ref33 ref32 ref2 ref1 ref39 ref38 ref70 ref24 ref68 ref23 ref67 ref26 ref25 ref69 ref20 ref64 ref63 ref22 ref66 ref21 ref65 ref28 ref27 ref29 ref60 ref62 ref61 |
| References_xml | – ident: ref5 doi: 10.1109/TEVC.2021.3060012 – ident: ref17 doi: 10.1109/CEC.2009.4983012 – ident: ref4 doi: 10.1007/978-3-319-42978-6_2 – ident: ref43 doi: 10.1109/TMAG.2004.825006 – ident: ref54 doi: 10.1109/CEC.2017.7969433 – ident: ref21 doi: 10.1007/s00500-010-0674-z – ident: ref64 doi: 10.1016/j.ins.2021.07.078 – ident: ref37 doi: 10.1109/TCYB.2021.3069814 – ident: ref32 doi: 10.1109/TCYB.2020.2989465 – ident: ref35 doi: 10.1016/j.asoc.2013.10.008 – ident: ref38 doi: 10.1631/jzus.C0910072 – ident: ref48 doi: 10.1109/TEVC.2007.902851 – ident: ref15 doi: 10.1109/TEVC.2004.831456 – ident: ref40 doi: 10.1109/CEC.2006.1688283 – ident: ref49 doi: 10.1109/TEVC.2009.2033582 – ident: ref8 doi: 10.1109/TEVC.2019.2951217 – ident: ref56 doi: 10.1007/978-3-642-21524-7_9 – ident: ref6 doi: 10.1016/j.swevo.2019.03.015 – ident: ref39 doi: 10.1109/4235.996017 – ident: ref29 doi: 10.1016/j.ins.2019.01.066 – ident: ref57 doi: 10.1109/TEVC.2007.913121 – ident: ref53 doi: 10.1007/s00158-008-0269-9 – ident: ref27 doi: 10.1007/978-3-319-31153-1_20 – ident: ref33 doi: 10.1109/TCYB.2019.2909806 – ident: ref31 doi: 10.1109/TCYB.2015.2490738 – ident: ref47 doi: 10.1016/j.ins.2013.03.002 – ident: ref26 doi: 10.3390/app8091673 – ident: ref63 doi: 10.1109/TEVC.2018.2855411 – ident: ref42 doi: 10.1109/TCYB.2015.2493239 – ident: ref12 doi: 10.1109/TEVC.2019.2958075 – ident: ref65 doi: 10.1109/TCYB.2020.3021138 – ident: ref1 doi: 10.1109/TCYB.2021.3112675 – ident: ref55 doi: 10.1109/TEVC.2019.2896967 – ident: ref69 doi: 10.1016/j.swevo.2011.02.002 – ident: ref14 doi: 10.1007/3-540-44719-9_20 – ident: ref24 doi: 10.1016/j.ins.2020.07.009 – ident: ref25 doi: 10.1109/TEVC.2016.2574621 – ident: ref52 doi: 10.1109/TCYB.2015.2510698 – ident: ref58 doi: 10.1109/TEM.2017.2766443 – ident: ref60 doi: 10.1016/j.asoc.2012.02.025 – ident: ref41 doi: 10.1109/4235.873238 – ident: ref68 doi: 10.1016/j.swevo.2021.100930 – ident: ref22 doi: 10.1109/TCYB.2017.2647742 – ident: ref50 doi: 10.1109/CEC.2010.5586396 – ident: ref2 doi: 10.1162/EVCO_a_00097 – ident: ref18 doi: 10.1145/2739480.2754708 – ident: ref61 doi: 10.3182/20120215-3-AT-3016.00066 – ident: ref28 doi: 10.1007/s00500-015-1820-4 – ident: ref44 doi: 10.1007/978-3-642-00619-7_7 – ident: ref23 doi: 10.1109/tevc.2023.3241762 – ident: ref10 doi: 10.1109/TEVC.2005.846356 – ident: ref62 doi: 10.1109/TEVC.2020.3004012 – ident: ref66 doi: 10.1109/tevc.2022.3222844 – ident: ref70 doi: 10.1109/TSMC.2022.3221466 – ident: ref7 doi: 10.1109/TEVC.2020.3004027 – ident: ref16 doi: 10.1016/j.ins.2022.05.050 – ident: ref34 doi: 10.1109/TEVC.2019.2925722 – ident: ref36 doi: 10.1109/TEVC.2008.2009032 – ident: ref45 doi: 10.1109/TSMC.2018.2858843 – ident: ref9 doi: 10.1109/TCYB.2020.3017017 – ident: ref11 doi: 10.1109/JSYST.2021.3061670 – ident: ref19 doi: 10.1109/TEVC.2011.2180533 – ident: ref67 doi: 10.1109/CEC.2006.1688406 – ident: ref30 doi: 10.1109/TCYB.2013.2245892 – ident: ref3 doi: 10.1109/HIS.2006.264943 – ident: ref13 doi: 10.1145/3524495 – ident: ref46 doi: 10.1109/TCYB.2014.2345478 – ident: ref51 doi: 10.1007/1-84628-137-7_6 – ident: ref20 doi: 10.1016/j.swevo.2017.10.005 – ident: ref59 doi: 10.1016/j.oceaneng.2017.12.049 |
| SSID | ssj0014519 |
| Score | 2.5309007 |
| Snippet | Dynamic constrained multiobjective optimization problems (DCMOPs) abound in real-world applications and gain increasing attention in the evolutionary... |
| SourceID | crossref ieee |
| SourceType | Enrichment Source Index Database Publisher |
| StartPage | 1381 |
| SubjectTerms | Benchmark testing Diversity Diversity reception dynamic constrained multiobjective optimization evolutionary algorithm Heuristic algorithms Linear programming Optimization Sociology Statistics test suite |
| Title | Evolutionary Dynamic Constrained Multiobjective Optimization: Test Suite and Algorithm |
| URI | https://ieeexplore.ieee.org/document/10246310 |
| Volume | 28 |
| WOSCitedRecordID | wos001328314100009&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: 1941-0026 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0014519 issn: 1089-778X databaseCode: RIE dateStart: 19970101 isFulltext: true titleUrlDefault: https://ieeexplore.ieee.org/ providerName: IEEE |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEF60eKgHq1WxvtiDJyF1k91ms95KbfFUBWvpLST78EGbSGgL_nv31ZKLgrew7EKSb2dnkpn5PgBupER6nGcByXkSEKlCbXPcSIYJqjLRQzzkVmyCjsfJbMaefbO67YWRUtriM9k1lzaXL0q-Mr_KtIVHJMamoWqX0tg1a21TBoYnxVXTMx0yJjOfwgwRu5sMp4Ou0QnvYhzi2Ei615xQTVXFOpVR65-3cwgOfPQI-w7uI7AjizZobZQZoDfUNtiv0Qy2QdNElI6Q-RhMh2u_27LqGz44QXpodDutWoQU0PbklvmnOwrhkz5UFr5b8x5OtBeBLysdqMKsELA_fyurj-X74gS8joaTwWPgxRUCrn3yMggVijUgJMcZxzmNBMtpjxrY4lglUtu5iEXGhQpzlMhE0Z5CBOcxJwpRlSt8ChpFWcgzAAXDLGGcRCRRhEiW6SDHcDlhFkaIR1EHoM3bTrlnHjePNE_tFwhiqQEoNQClHqAOuN0u-XK0G39NPjHg1CY6XM5_Gb8ATb2cuJK8S9BYVit5Bfb4WkNRXdtd9QO_k8up |
| linkProvider | IEEE |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEB6kCtaD1VqxPvfgSUjdZLdJ1luplYq1CtbSW0j24YM-pLQF_727m23pRcFbWDYhybezM8nMNx_ApZRYj_PUoxmPPSqVr22OG8kwEalU1DH3uRWbiLrdeDBgz46sbrkwUkpbfCZr5tDm8sWEz82vMm3hAQ2JIVRtGuksR9daJQ1Mp5S8np7poDEeuCSmj9l1r9Vv1oxSeI0Qn4RG1H3NDa3pqli3clf65w3twa6LH1EjB3wfNuS4DKWlNgNyplqGnbVGg2Uompgyb8l8AP3Wwq23dPqNbnNJemSUO61ehBTIsnIn2We-GaInva2MHF_zBvW0H0Evcx2qonQsUGP4Npl-zN5HFXi9a_Wabc_JK3hce-WZ5yscakhoRlJOsigQLIvqkQEuDFUstaWLUKRcKD_DsYxVVFeYkizkVOFIZYocQmE8GcsjQIIRFjNOAxorSiVLdZhjujkR5geYB0EV8PJtJ9z1HjePNEzsNwhmiQEoMQAlDqAqXK1O-cobb_w1uWLAWZuY43L8y_gFbLd7j52kc999OIGivhTNC_ROoTCbzuUZbPGFhmV6blfYD4cJzvI |
| 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=Evolutionary+Dynamic+Constrained+Multiobjective+Optimization%3A+Test+Suite+and+Algorithm&rft.jtitle=IEEE+transactions+on+evolutionary+computation&rft.au=Chen%2C+Guoyu&rft.au=Guo%2C+Yinan&rft.au=Wang%2C+Yong&rft.au=Liang%2C+Jing&rft.date=2024-10-01&rft.issn=1089-778X&rft.eissn=1941-0026&rft.volume=28&rft.issue=5&rft.spage=1381&rft.epage=1395&rft_id=info:doi/10.1109%2FTEVC.2023.3313689&rft.externalDBID=n%2Fa&rft.externalDocID=10_1109_TEVC_2023_3313689 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1089-778X&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1089-778X&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1089-778X&client=summon |