New Knowledge-based Genetic Algorithm for Excavator Boom Structural Optimization
Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is prop...
Uloženo v:
| Vydáno v: | Chinese journal of mechanical engineering Ročník 27; číslo 2; s. 392 - 401 |
|---|---|
| Hlavní autoři: | , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Beijing
Chinese Mechanical Engineering Society
01.03.2014
Springer Nature B.V School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China |
| Vydání: | English ed. |
| Témata: | |
| ISSN: | 1000-9345, 2192-8258 |
| On-line přístup: | Získat plný text |
| Tagy: |
Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
|
| Abstract | Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the conflgurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, arc taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem. |
|---|---|
| AbstractList | Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem. Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the configurability of knowledge and genetic operators. Then, the new knowledge based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, are taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multilevel knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem. Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the conflgurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, arc taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem. |
| Author | HUA Haiyan LIN Shuwen |
| AuthorAffiliation | School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China |
| AuthorAffiliation_xml | – name: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China |
| Author_xml | – sequence: 1 givenname: Haiyan surname: Hua fullname: Hua, Haiyan organization: School of Mechanical Engineering and Automation, Fuzhou University – sequence: 2 givenname: Shuwen surname: Lin fullname: Lin, Shuwen email: lsw@fzu.edu.cn organization: School of Mechanical Engineering and Automation, Fuzhou University |
| BookMark | eNp9kUlv2zAQhYkiBeqkvfeoopfmIJeLKIvH1HDTJUuB5k6Q1EihIZEOSdVuf33pOEiAHHLhMnjfvIeZY3TkvAOE3hM8ZwKTz8sfl6s5xaSaY5or9BWaUSJo2VDeHKEZwRiXglX8DTqOcZ1_NSHNDP26gm3x0_ntAG0PpVYR2uIcHCRrirOh98Gm27HofChWO6P-qJRfX7wfi98pTCZNQQ3F9SbZ0f5TyXr3Fr3u1BDh3cN9gm6-rm6W38qL6_Pvy7OL0lS4TiVlbQONAOCm46puseGswwC6M1oQRZUyVatVpxeaARGtalvNdcMVp63GwE7Q6aHtVrlOuV6u_RRcNpTrXW92WsJ-FjgfIms_HbSb4O8miEmONhoYBuXAT1GSmhPWLKqmytKPz6SPfSnlglFWC5xV-KAywccYoJObYEcV_kqC5X4bcr8NuU8gMc0VmpH6GWJsuh9YCsoOL4HkAMbs4XoIT4leYD48mN16199l7DFg1Qi8qMSC_QeUgqx6 |
| CitedBy_id | crossref_primary_10_1088_1757_899X_269_1_012048 crossref_primary_10_1007_s12008_025_02287_6 crossref_primary_10_1016_j_compag_2020_105278 crossref_primary_10_1109_TMECH_2019_2903140 crossref_primary_10_1016_j_autcon_2022_104486 |
| Cites_doi | 10.3901/CJME.2011.01.042 10.1007/11572961_43 10.3901/CJME.2008.06.018 10.4028/www.scientific.net/AMR.479-481.1851 10.1016/j.compstruc.2007.11.006 10.1016/j.eswa.2011.12.012 10.3901/JME.2002.01.051 10.1016/j.eswa.2010.09.002 10.3901/CJME.2010.05.537 10.1109/TEVC.2003.817236 10.3901/JME.2010.16.136 10.1016/j.procs.2010.04.152 10.3901/CJME.2010.04.484 10.3901/CJME.2012.02.255 |
| ContentType | Journal Article |
| Copyright | Chinese Mechanical Engineering Society and Springer-Verlag Berlin Heidelberg 2014 Chinese Journal of Mechanical Engineering is a copyright of Springer, (2014). All Rights Reserved. Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Chinese Mechanical Engineering Society and Springer-Verlag Berlin Heidelberg 2014 – notice: Chinese Journal of Mechanical Engineering is a copyright of Springer, (2014). All Rights Reserved. – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2RA 92L CQIGP W92 ~WA AAYXX CITATION 8FE 8FG ABJCF ABUWG AFKRA AZQEC BENPR BGLVJ CCPQU DWQXO HCIFZ L6V M7S PHGZM PHGZT PIMPY PKEHL PQEST PQGLB PQQKQ PQUKI PRINS PTHSS 7SC 7TB 8FD FR3 JQ2 L7M L~C L~D 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.3901/CJME.2014.02.392 |
| DatabaseName | 维普期刊资源整合服务平台 中文科技期刊数据库-CALIS站点 维普中文期刊数据库 中文科技期刊数据库-工程技术 中文科技期刊数据库- 镜像站点 CrossRef ProQuest SciTech Collection ProQuest Technology Collection Materials Science & Engineering Collection ProQuest Central (Alumni) ProQuest Central UK/Ireland ProQuest Central Essentials ProQuest Central Technology collection ProQuest One ProQuest Central SciTech Premium Collection ProQuest Engineering Collection Engineering Database Proquest Central Premium ProQuest One Academic Publicly Available Content Database ProQuest One Academic Middle East (New) ProQuest One Academic Eastern Edition (DO NOT USE) One Applied & Life Sciences ProQuest One Academic (retired) ProQuest One Academic UKI Edition ProQuest Central China Engineering Collection Computer and Information Systems Abstracts Mechanical & Transportation Engineering Abstracts Technology Research Database Engineering Research Database ProQuest Computer Science Collection Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Academic Computer and Information Systems Abstracts Professional Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitle | CrossRef Publicly Available Content Database Engineering Database Technology Collection ProQuest One Academic Middle East (New) ProQuest Central Essentials ProQuest One Academic Eastern Edition ProQuest Central (Alumni Edition) SciTech Premium Collection ProQuest One Community College ProQuest Technology Collection ProQuest SciTech Collection ProQuest Central China ProQuest Central ProQuest One Applied & Life Sciences ProQuest Engineering Collection ProQuest One Academic UKI Edition ProQuest Central Korea Materials Science & Engineering Collection ProQuest Central (New) ProQuest One Academic ProQuest One Academic (New) Engineering Collection Technology Research Database Computer and Information Systems Abstracts – Academic Mechanical & Transportation Engineering Abstracts ProQuest Computer Science Collection Computer and Information Systems Abstracts Engineering Research Database Advanced Technologies Database with Aerospace Computer and Information Systems Abstracts Professional |
| DatabaseTitleList | Technology Research Database Publicly Available Content Database |
| Database_xml | – sequence: 1 dbid: PIMPY name: Publicly Available Content Database url: http://search.proquest.com/publiccontent sourceTypes: Aggregation Database |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| DocumentTitleAlternate | New Knowledge-based Genetic Algorithm for Excavator Boom Structural Optimization |
| EISSN | 2192-8258 |
| Edition | English ed. |
| EndPage | 401 |
| ExternalDocumentID | jxgcxb_e201402019 10_3901_CJME_2014_02_392 48907497 |
| GrantInformation_xml | – fundername: National Natural Science Foundation of China funderid: (Grant 51175086) |
| GroupedDBID | -03 -0C -SC -S~ 06D 0R~ 0VY 29B 29~ 2B. 2C0 2RA 30V 4.4 5VR 8FE 8FG 92H 92I 92L 92M 96X 9D9 9DC AAIAL AAJKR AAKKN AARHV AARTL AAWCG AAYIU AAYQN AAYTO AAYZJ AAZMS ABFTD ABJCF ABJOX ABTHY ABTMW ACACY ACCUX ACGFS ACKNC ADBBV ADHIR ADINQ AEBTG AEGNC AEJHL AENEX AEOHA AEPYU AETCA AFGXO AFKRA AFLOW AFNRJ AFUIB AFWTZ AFZKB AGAYW AGQMX AGWZB AGYKE AHAVH AHBXF AHBYD AHKAY AHSBF AIIXL AJBLW AJRNO AKLTO ALFXC ALMA_UNASSIGNED_HOLDINGS AMKLP ANMIH AUKKA BAPOH BCNDV BENPR BGLVJ BGNMA C24 C6C CAJUS CCEZO CCPQU CEKLB CHBEP CQIGP CS3 CW9 DU5 EBS EJD ESBYG FA0 FIGPU FRRFC FYJPI GGRSB GQ6 GQ7 GROUPED_DOAJ HCIFZ HF~ HMJXF HRMNR HZ~ I0C JBSCW JUIAU KOV L6V M4Y M7S NU0 O9- OK1 PIMPY PROAC PTHSS Q-- Q-2 R-C RLLFE RSV RT3 SCL SDH SEG SHX SNX SOJ T8S TCJ TGT U1F U1G U2A U5C U5M UG4 VC2 W48 W92 ~A9 ~M1 ~WA AAXDM ABEEZ ACULB ADMLS AFBBN CAJEC AALRI AAXUO AAYXX ABFSG ACSTC AEZWR AFFHD AFHIU AHWEU AIXLP CITATION FDB PHGZM PHGZT PQGLB ABUWG AZQEC DWQXO PKEHL PQEST PQQKQ PQUKI PRINS 7SC 7TB 8FD FR3 JQ2 L7M L~C L~D PUEGO 4A8 93N PMFND PSX |
| ID | FETCH-LOGICAL-c406t-23d8e89ee5cf5a6d0c53f0eebfcb91a2aac4dbafb7b3e19daddb5b85a52db0e3 |
| IEDL.DBID | M7S |
| ISICitedReferencesCount | 7 |
| ISICitedReferencesURI | http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000332496900019&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D |
| ISSN | 1000-9345 |
| IngestDate | Thu May 29 04:10:07 EDT 2025 Fri Sep 05 12:34:35 EDT 2025 Wed Oct 08 14:30:43 EDT 2025 Sat Nov 29 02:57:43 EST 2025 Tue Nov 18 22:37:19 EST 2025 Fri Feb 21 02:29:05 EST 2025 Wed Feb 14 10:37:45 EST 2024 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 2 |
| Keywords | dual evolution mechanism domain culture boom structural optimization deep implicit knowledge knowledge-based genetic strategies |
| Language | English |
| LinkModel | DirectLink |
| MergedId | FETCHMERGED-LOGICAL-c406t-23d8e89ee5cf5a6d0c53f0eebfcb91a2aac4dbafb7b3e19daddb5b85a52db0e3 |
| Notes | boom structural optimization, dual evolution mechanism, knowledge-based genetic strategies, deep implicit knowledge, domain culture 11-2737/TH Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom structural optimization problem. To improve the optimization efficiency and quality, a new knowledge-based real-coded genetic algorithm is proposed. A dual evolution mechanism combining knowledge evolution with genetic algorithm is established to extract, handle and utilize the shallow and deep implicit constraint knowledge to guide the optimal searching of genetic algorithm circularly. Based on this dual evolution mechanism, knowledge evolution and population evolution can be connected by knowledge influence operators to improve the conflgurability of knowledge and genetic operators. Then, the new knowledge-based selection operator, crossover operator and mutation operator are proposed to integrate the optimal process knowledge and domain culture to guide the excavator boom structural optimization. Eight kinds of testing algorithms, which include different genetic operators, arc taken as examples to solve the structural optimization of a medium-sized excavator boom. By comparing the results of optimization, it is shown that the algorithm including all the new knowledge-based genetic operators can more remarkably improve the evolutionary rate and searching ability than other testing algorithms, which demonstrates the effectiveness of knowledge for guiding optimal searching. The proposed knowledge-based genetic algorithm by combining multi-level knowledge evolution with numerical optimization provides a new effective method for solving the complex engineering optimization problem. ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23 |
| OpenAccessLink | https://www.proquest.com/docview/2259323690?pq-origsite=%requestingapplication% |
| PQID | 2259323690 |
| PQPubID | 4406321 |
| PageCount | 10 |
| ParticipantIDs | wanfang_journals_jxgcxb_e201402019 proquest_miscellaneous_1651387484 proquest_journals_2259323690 crossref_primary_10_3901_CJME_2014_02_392 crossref_citationtrail_10_3901_CJME_2014_02_392 springer_journals_10_3901_CJME_2014_02_392 chongqing_primary_48907497 |
| PublicationCentury | 2000 |
| PublicationDate | 2014-03-01 |
| PublicationDateYYYYMMDD | 2014-03-01 |
| PublicationDate_xml | – month: 03 year: 2014 text: 2014-03-01 day: 01 |
| PublicationDecade | 2010 |
| PublicationPlace | Beijing |
| PublicationPlace_xml | – name: Beijing – name: Heidelberg |
| PublicationTitle | Chinese journal of mechanical engineering |
| PublicationTitleAbbrev | Chin. J. Mech. Eng |
| PublicationTitleAlternate | Chinese Journal of Mechanical Engineering |
| PublicationTitle_FL | Chinese Journal of Mechanical Engineering |
| PublicationYear | 2014 |
| Publisher | Chinese Mechanical Engineering Society Springer Nature B.V School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China |
| Publisher_xml | – name: Chinese Mechanical Engineering Society – name: Springer Nature B.V – name: School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou 350108, China |
| References | Wei, Li, Li (CR8) 2010; 46 (CR20) 1988 Wei, Zhao, Ju (CR3) 2011; 24 Ei-Hosseini, Hassanien, Abraham (CR15) 2008 Hua, Lin, Shen (CR17) 2012; 479–481 Togan, Daloglu (CR19) 2008; 86 Zhang, Wang (CR5) 2002; 38 Zhao, Wang, Zeng (CR7) 2012; 39 Wang (CR2) 2010; 23 Ren, San (CR9) 2007; 35 Wei, Fan, Xu (CR10) 2008; 21 Olcer (CR1) 2008; 32 Suresh, Mani, Omkar (CR4) 2005; 3740 Lai, Dai, Bai (CR14) 2012; 25 Zhou (CR6) 2003 Farmani, Wright (CR18) 2003; 7 Prakash, Chan, Deshmukh (CR13) 2011; 38 Wendt, Cortes, Margalef (CR12) 2010; 1 Zhao, Wang, Wang (CR11) 2010; 23 Wang, Li, Shi (CR16) 2010; 21 J Q Zhao (392_CR7) 2012; 39 H Hua (392_CR17) 2012; 479–481 W Wang (392_CR16) 2010; 21 Binggang Wang (392_CR2) 2010; 23 M Zhang (392_CR5) 2002; 38 R Farmani (392_CR18) 2003; 7 Y Zhao (392_CR11) 2010; 23 Tianjin Mechanical Engineering Research Institute. (392_CR20) 1988 S Suresh (392_CR4) 2005; 3740 K Wendt (392_CR12) 2010; 1 V Togan (392_CR19) 2008; 86 A L Olcer (392_CR1) 2008; 32 A Prakash (392_CR13) 2011; 38 X Wei (392_CR3) 2011; 24 M A Ei-Hosseini (392_CR15) 2008 Z Ren (392_CR9) 2007; 35 Y Lai (392_CR14) 2012; 25 L Wei (392_CR8) 2010; 46 T Wei (392_CR10) 2008; 21 Yonghua Zhou (392_CR6) 2003 |
| References_xml | – volume: 24 start-page: 42 issue: 1 year: 2011 end-page: 49 ident: CR3 article-title: Multi-objective optimization conceptual design of product structure based on variable length gene expression[J] publication-title: Chinese Journal of Mechanical Engineering doi: 10.3901/CJME.2011.01.042 – year: 1988 ident: CR20 publication-title: Hydraulic excavators-testing method of structure strength – start-page: 448 year: 2008 end-page: 493 ident: CR15 article-title: Cultural-based genetic algorithm: design and real world applications[C] publication-title: , Kaohsiung, Nov. 26–28 – volume: 3740 start-page: 529 year: 2005 end-page: 539 ident: CR4 article-title: A real coded genetic algorithm for data partitioning and scheduling in networks with arbitrary processor release time[J] publication-title: Advances in Computer Systems Architecture doi: 10.1007/11572961_43 – volume: 21 start-page: 18 issue: 6 year: 2008 end-page: 24 ident: CR10 article-title: Greedy non-dominated sorting in genetic algorithm-II for vehicle routing problem in distribution[J] publication-title: Chinese Journal of Mechanical Engineering doi: 10.3901/CJME.2008.06.018 – volume: 21 start-page: 303 issue: 3 year: 2010 end-page: 309 ident: CR16 article-title: An effective cultural genetic algorithm for job shop scheduling problem[J] publication-title: China Mechanical Engineering – volume: 479–481 start-page: 1851 year: 2012 end-page: 1856 ident: CR17 article-title: A new method of the constraints expression and handling for excavator boom structural optimization[C] publication-title: Advanced Materials Research doi: 10.4028/www.scientific.net/AMR.479-481.1851 – volume: 86 start-page: 1204 issue: 11–12 year: 2008 end-page: 1218 ident: CR19 article-title: An improved genetic algorithm with initial population strategy and self-adaptive member grouping[J] publication-title: Computers and Structures doi: 10.1016/j.compstruc.2007.11.006 – volume: 39 start-page: 6041 issue: 5 year: 2012 end-page: 6051 ident: CR7 article-title: An effective hybrid genetic algorithm with flexible allowance technique for constrained engineering design optimization[J] publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2011.12.012 – volume: 38 start-page: 51 issue: 1 year: 2002 end-page: 54 ident: CR5 article-title: Shape optimization using an adaptive crossover operator[J] publication-title: Chinese Journal of Mechanical Engineering doi: 10.3901/JME.2002.01.051 – year: 2003 ident: CR6 publication-title: Research on combination of crossover operators of real coded genetic algorithms[D] – volume: 38 start-page: 3161 issue: 4 year: 2011 end-page: 3171 ident: CR13 article-title: FMS scheduling with knowledge based genetic algorithm approach[J] publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2010.09.002 – volume: 23 start-page: 537 issue: 5 year: 2010 end-page: 546 ident: CR2 article-title: Sequencing mixed-model production systems by modified multi-objective genetic algorithms[J] publication-title: Chinese Journal of Mechanical Engineering doi: 10.3901/CJME.2010.05.537 – volume: 7 start-page: 445 issue: 5 year: 2003 end-page: 455 ident: CR18 article-title: Self-adaptive fitness formulation for constrained optimization[J] publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2003.817236 – volume: 35 start-page: 269 issue: 2 year: 2007 end-page: 274 ident: CR9 article-title: Improvement of real-valued genetic algorithm and performance study[J] publication-title: Acta Electronica Sinica – volume: 32 start-page: 86 issue: 1 year: 2008 end-page: 97 ident: CR1 article-title: A hybrid approach for multi-objective combinatorial optimization problems in ship design and shipping[J] publication-title: Computers and Operations Research – volume: 46 start-page: 136 issue: 16 year: 2010 end-page: 141 ident: CR8 article-title: Optimization of tandem cold rolling schedule based on improved adaptive genetic algorithm[J] publication-title: Journal of Mechanical Engineering doi: 10.3901/JME.2010.16.136 – volume: 1 start-page: 1 367 issue: 1 year: 2010 end-page: 1 375 ident: CR12 article-title: Knowledge-guided genetic algorithm for input parameter optimisation in environmental modelling[J] publication-title: Procedia Computer Science doi: 10.1016/j.procs.2010.04.152 – volume: 23 start-page: 484 issue: 4 year: 2010 end-page: 495 ident: CR11 article-title: New hybrid parallel algorithm for variable-sized batch splitting scheduling with alternative machines in job shops[J] publication-title: Chinese Journal of Mechanical Engineering doi: 10.3901/CJME.2010.04.484 – volume: 25 start-page: 255 issue: 2 year: 2012 end-page: 261 ident: CR14 article-title: Discrete variable structural optimization based on multidirectional fuzzy genetic algorithm[J] publication-title: Chinese Journal of Mechanical Engineering doi: 10.3901/CJME.2012.02.255 – volume: 23 start-page: 484 issue: 4 year: 2010 ident: 392_CR11 publication-title: Chinese Journal of Mechanical Engineering doi: 10.3901/CJME.2010.04.484 – volume: 23 start-page: 537 issue: 5 year: 2010 ident: 392_CR2 publication-title: Chinese Journal of Mechanical Engineering doi: 10.3901/CJME.2010.05.537 – volume: 24 start-page: 42 issue: 1 year: 2011 ident: 392_CR3 publication-title: Chinese Journal of Mechanical Engineering doi: 10.3901/CJME.2011.01.042 – volume: 86 start-page: 1204 issue: 11–12 year: 2008 ident: 392_CR19 publication-title: Computers and Structures doi: 10.1016/j.compstruc.2007.11.006 – volume: 21 start-page: 303 issue: 3 year: 2010 ident: 392_CR16 publication-title: China Mechanical Engineering – volume: 21 start-page: 18 issue: 6 year: 2008 ident: 392_CR10 publication-title: Chinese Journal of Mechanical Engineering doi: 10.3901/CJME.2008.06.018 – volume: 7 start-page: 445 issue: 5 year: 2003 ident: 392_CR18 publication-title: IEEE Transactions on Evolutionary Computation doi: 10.1109/TEVC.2003.817236 – start-page: 448 volume-title: Eighth International Conference on Intelligent Systems Design and Applications, Kaohsiung, Nov. 26–28 year: 2008 ident: 392_CR15 – volume-title: Hydraulic excavators-testing method of structure strength year: 1988 ident: 392_CR20 – volume: 46 start-page: 136 issue: 16 year: 2010 ident: 392_CR8 publication-title: Journal of Mechanical Engineering doi: 10.3901/JME.2010.16.136 – volume: 25 start-page: 255 issue: 2 year: 2012 ident: 392_CR14 publication-title: Chinese Journal of Mechanical Engineering doi: 10.3901/CJME.2012.02.255 – volume-title: Research on combination of crossover operators of real coded genetic algorithms[D] year: 2003 ident: 392_CR6 – volume: 3740 start-page: 529 year: 2005 ident: 392_CR4 publication-title: Advances in Computer Systems Architecture doi: 10.1007/11572961_43 – volume: 38 start-page: 3161 issue: 4 year: 2011 ident: 392_CR13 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2010.09.002 – volume: 1 start-page: 1 367 issue: 1 year: 2010 ident: 392_CR12 publication-title: Procedia Computer Science doi: 10.1016/j.procs.2010.04.152 – volume: 38 start-page: 51 issue: 1 year: 2002 ident: 392_CR5 publication-title: Chinese Journal of Mechanical Engineering doi: 10.3901/JME.2002.01.051 – volume: 32 start-page: 86 issue: 1 year: 2008 ident: 392_CR1 publication-title: Computers and Operations Research – volume: 39 start-page: 6041 issue: 5 year: 2012 ident: 392_CR7 publication-title: Expert Systems with Applications doi: 10.1016/j.eswa.2011.12.012 – volume: 35 start-page: 269 issue: 2 year: 2007 ident: 392_CR9 publication-title: Acta Electronica Sinica – volume: 479–481 start-page: 1851 year: 2012 ident: 392_CR17 publication-title: Advanced Materials Research doi: 10.4028/www.scientific.net/AMR.479-481.1851 |
| SSID | ssj0006118 |
| Score | 1.9676018 |
| Snippet | Due to the insufficiency of utilizing knowledge to guide the complex optimal searching, existing genetic algorithms fail to effectively solve excavator boom... |
| SourceID | wanfang proquest crossref springer chongqing |
| SourceType | Aggregation Database Enrichment Source Index Database Publisher |
| StartPage | 392 |
| SubjectTerms | Boom Crossovers Electrical Machines and Networks Electronics and Microelectronics Engineering Engineering Thermodynamics Evolution Evolutionary algorithms Excavators Genetic algorithms Heat and Mass Transfer Instrumentation Knowledge Knowledge base Knowledge bases (artificial intelligence) Machines Manufacturing Mechanical Engineering Operators Optimization Power Electronics Processes Search algorithms Searching Theoretical and Applied Mechanics 优化问题 动臂结构 实数编码遗传算法 工艺知识 挖掘机 测试算法 结构优化 进化机制 |
| Title | New Knowledge-based Genetic Algorithm for Excavator Boom Structural Optimization |
| URI | http://lib.cqvip.com/qk/85891X/201402/48907497.html https://link.springer.com/article/10.3901/CJME.2014.02.392 https://www.proquest.com/docview/2259323690 https://www.proquest.com/docview/1651387484 https://d.wanfangdata.com.cn/periodical/jxgcxb-e201402019 |
| Volume | 27 |
| WOSCitedRecordID | wos000332496900019&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: PRVPQU databaseName: Engineering Database customDbUrl: eissn: 2192-8258 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0006118 issn: 1000-9345 databaseCode: M7S dateStart: 20120101 isFulltext: true titleUrlDefault: http://search.proquest.com providerName: ProQuest – providerCode: PRVPQU databaseName: ProQuest Central customDbUrl: eissn: 2192-8258 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0006118 issn: 1000-9345 databaseCode: BENPR dateStart: 20120101 isFulltext: true titleUrlDefault: https://www.proquest.com/central providerName: ProQuest – providerCode: PRVPQU databaseName: Publicly Available Content Database customDbUrl: eissn: 2192-8258 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0006118 issn: 1000-9345 databaseCode: PIMPY dateStart: 20120101 isFulltext: true titleUrlDefault: http://search.proquest.com/publiccontent providerName: ProQuest |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwpV1Lb9QwEB5BiwQceCNSSmUQF5CiteMka59QW20FSF1F0EM5WbbjLK262ZZdqv35zOS15VAuHJNMnMc3nofHMwPwfszLKtc2jzXXSZxWQaIc5CourbK5shmqVNs0mxhPp-r0VBfdgtuy21bZy8RGUJcLT2vkI-Q7NDUkOnOfLq9i6hpF0dWuhcZd2KYqCaLZuvd9kMS5EG0qHOexlmnWhinJyx8dfj2e0MaulCp2SgqE3kdxU8-uUGH8raI2ducQKm0SfOrK1rMbuujo8f9-xRN41FmhbL9lm6dwJ9TP4OGN2oTPoUDxx4YVt5i0XcmQ2yjpkdmLGY66-jlnaPOysPb2mpx3hib7nLUlaamcB1ugQJp3mZ4v4ORocnL4Oe7aL8QetfwqTmSpgtIhZL7KbF5yn8mKh-Aq77SwibU-LZ2t3NjJIHSJktJlTmUIcOl4kC9hq17U4RUw4awku8-nSZUGdPC858GnGhW0sIrrCHaGn28u2yobJlXkt-txBKMeDeO7uuXUPuPCoP9CWBrC0hCWhid4Jongw3BHP9rttLs9UKabvUuzQSmCt8NlnHcUTLF1WPxeGpFnQiqqxBrBx54xNkPc_rx3HetsaM_XM792JhAZWu9C7_z7pV7DAyJtd8LtwhaiGt7APX-9Olv-2oPtg8m0-LbXTAU8Kr4cFz_-ALPfEaw |
| linkProvider | ProQuest |
| linkToHtml | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMw1V1Lb9QwEB6VgtT2wLsipYBBcAApWidOsvYBIVRatbRdVWIPvVm242yLutm2u5TlR_Efmclry6HceuCadZxk_fmbl2cG4G2f50WmTBYqruIwKbxAHuQyzI00mTQpilRTNZvoDwby-FgdLcHvNheGjlW2nFgRdT5x5CPvIe5Q1RBozH06vwipaxRFV9sWGjUs9v2vn2iyTT_ufcH1fRfHO9vDrd2w6SoQOhReszAWufRSeZ-6IjVZzl0qCu69LZxVkYmNcUluTWH7VvhI5UgANrUyxffOLfcCp70Dd1GLiFV1UvBbR_xZFNWZd5yHSiRpHRUlp0Jv6-vhNp0jS6hAqKC46wqyWzm6QPn0t0RcqLldZLbKJyoLU46uib6dB__Zn_YQ7jc6Nvtcb4pHsOTLx7B2rfLiEzhCcmedPzEkWZ4z3EuU0snM2Qg_YnYyZqjRMz935opcEwwNkjGrC-5SsRI2QbodN3msT2F4G1-0DsvlpPTPgEXWCNJqXRIXiUfz1TnuXaJQ_YiM5CqAjW6t9XldQ0QnkrwSqh9Ar1187Zqq7NQc5EyjdUbQ0QQdTdDRPMYrcQDvuzva2W4eu9niQjfcNNULUATwuvsZWYVCRab0kx9THWVpJCTVmQ3gQ4vDxRQ3P-9Ng9TF2O_zkZtb7WkY2iaR2vj3S72Cld3h4YE-2BvsP4dVuq0-87cJy7jC_gXcc1ez0-nly2r3MdC3DNw_cJFxcg |
| 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=New+knowledge-based+genetic+algorithm+for+excavator+boom+structural+optimization&rft.jtitle=Chinese+journal+of+mechanical+engineering&rft.au=Hua%2C+Haiyan&rft.au=Lin%2C+Shuwen&rft.date=2014-03-01&rft.issn=1000-9345&rft.eissn=2192-8258&rft.volume=27&rft.issue=2&rft.spage=392&rft.epage=401&rft_id=info:doi/10.3901%2FCJME.2014.02.392&rft.externalDBID=n%2Fa&rft.externalDocID=10_3901_CJME_2014_02_392 |
| thumbnail_s | http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fimage.cqvip.com%2Fvip1000%2Fqk%2F85891X%2F85891X.jpg http://cvtisr.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fjxgcxb-e%2Fjxgcxb-e.jpg |