An Inverse Power Generation Mechanism Based Fruit Fly Algorithm for Function Optimization View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Ao Liu, Xudong Deng, Liang Ren, Ying Liu, Bo Liu

ABSTRACT

As a novel population-based optimization algorithm, fruit fly optimization (FFO) algorithm is inspired by the foraging behavior of fruit flies and possesses the advantages of simple search operations and easy implementation. Just like most population-based evolutionary algorithms, the basic FFO also suffers from being trapped in local optima for function optimization due to premature convergence. In this paper, an improved FFO, named IPGS-FFO, is proposed in which two novel strategies are incorporated into the conventional FFO. Specifically, a smell sensitivity parameter together with an inverse power generation mechanism (IPGS) is introduced to enhance local exploitation. Moreover, a dynamic shrinking search radius strategy is incorporated so as to enhance the global exploration over search space by adaptively adjusting the searching area in the problem domain. The statistical performance of FFO, the proposed IPGS-FFO, three state-of-the-art FFO variants, and six metaheuristics are tested on twenty-six well-known unimodal and multimodal benchmark functions with dimension 30, respectively. Experimental results and comparisons show that the proposed IPGS-FFO achieves better performance than three FFO variants and competitive performance against six other meta-heuristics in terms of the solution accuracy and convergence rate. More... »

PAGES

634-656

References to SciGraph publications

  • 2019-03. An effective soft computing technology based on belief-rule-base and particle swarm optimization for tipping paper permeability measurement in JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
  • 2016-02. Multi-Verse Optimizer: a nature-inspired algorithm for global optimization in NEURAL COMPUTING AND APPLICATIONS
  • 2011-06. A unified framework for population-based metaheuristics in ANNALS OF OPERATIONS RESEARCH
  • 1997-12. Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces in JOURNAL OF GLOBAL OPTIMIZATION
  • 1997-03. Nonlinear Programming in JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY
  • 2014. An Enhanced Estimation of Distribution Algorithm for No-Wait Job Shop Scheduling Problem with Makespan Criterion in INTELLIGENT COMPUTING METHODOLOGIES
  • 2016-12. Parameter estimation of nonlinear chaotic system by improved TLBO strategy in SOFT COMPUTING
  • 2016-07. A novel 3D fruit fly optimization algorithm and its applications in economics in NEURAL COMPUTING AND APPLICATIONS
  • 2016. Hybrid Estimation of Distribution Algorithm for No-Wait Flow-Shop Scheduling Problem with Sequence-Dependent Setup Times and Release Dates in INTELLIGENT COMPUTING THEORIES AND APPLICATION
  • 2016. An Improved Quantum-Inspired Evolution Algorithm for No-Wait Flow Shop Scheduling Problem to Minimize Makespan in INTELLIGENT COMPUTING THEORIES AND APPLICATION
  • 2009. Firefly Algorithms for Multimodal Optimization in STOCHASTIC ALGORITHMS: FOUNDATIONS AND APPLICATIONS
  • 1988-10. Genetic Algorithms and Machine Learning in MACHINE LEARNING
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/s11424-018-7250-5

    DOI

    http://dx.doi.org/10.1007/s11424-018-7250-5

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1110065333


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "about": [
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Artificial Intelligence and Image Processing", 
            "type": "DefinedTerm"
          }, 
          {
            "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
            "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
            "name": "Information and Computing Sciences", 
            "type": "DefinedTerm"
          }
        ], 
        "author": [
          {
            "affiliation": {
              "alternateName": "Wuhan University of Science and Technology", 
              "id": "https://www.grid.ac/institutes/grid.412787.f", 
              "name": [
                "School of Management, Wuhan University of Science and Technology, 430065, Wuhan, China", 
                "Center for Service Science and Engineering, Wuhan University of Science and Technology, 430065, Wuhan, China", 
                "Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, 430065, Wuhan, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Liu", 
            "givenName": "Ao", 
            "id": "sg:person.010077612142.24", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010077612142.24"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Wuhan University of Science and Technology", 
              "id": "https://www.grid.ac/institutes/grid.412787.f", 
              "name": [
                "School of Management, Wuhan University of Science and Technology, 430065, Wuhan, China", 
                "Center for Service Science and Engineering, Wuhan University of Science and Technology, 430065, Wuhan, China", 
                "Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, 430065, Wuhan, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Deng", 
            "givenName": "Xudong", 
            "id": "sg:person.010675172542.42", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010675172542.42"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Wuhan University of Science and Technology", 
              "id": "https://www.grid.ac/institutes/grid.412787.f", 
              "name": [
                "School of Management, Wuhan University of Science and Technology, 430065, Wuhan, China", 
                "Center for Service Science and Engineering, Wuhan University of Science and Technology, 430065, Wuhan, China", 
                "Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, 430065, Wuhan, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Ren", 
            "givenName": "Liang", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Beihang University", 
              "id": "https://www.grid.ac/institutes/grid.64939.31", 
              "name": [
                "School of Economics and Management, Beihang University, 100191, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Liu", 
            "givenName": "Ying", 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Academy of Mathematics and Systems Science", 
              "id": "https://www.grid.ac/institutes/grid.458463.8", 
              "name": [
                "Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 100190, Beijing, China"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Liu", 
            "givenName": "Bo", 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1080/00207543.2016.1170226", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1000024513"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-642-04944-6_14", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1003585062", 
              "https://doi.org/10.1007/978-3-642-04944-6_14"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ins.2015.09.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1004480062"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/0895-7177(93)90204-c", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1005564211"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.knosys.2012.08.015", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1007216168"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1022602019183", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1009899114", 
              "https://doi.org/10.1023/a:1022602019183"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-42291-6_51", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011501282", 
              "https://doi.org/10.1007/978-3-319-42291-6_51"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cie.2015.05.022", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012078768"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.knosys.2015.09.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012647911"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1023/a:1008202821328", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1012950914", 
              "https://doi.org/10.1023/a:1008202821328"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.amc.2015.07.030", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013239196"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.amc.2007.09.004", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014039403"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-42291-6_54", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014667151", 
              "https://doi.org/10.1007/978-3-319-42291-6_54"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.eswa.2015.01.048", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1014936911"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.compstruc.2016.11.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015343268"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cor.2014.10.008", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1017728181"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-015-1942-8", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019565319", 
              "https://doi.org/10.1007/s00521-015-1942-8"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1155/2013/108768", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019623677"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1108/k-02-2014-0024", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1020117544"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.asoc.2016.11.023", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023753641"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10479-011-0894-3", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024832611", 
              "https://doi.org/10.1007/s10479-011-0894-3"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.knosys.2013.04.003", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025030139"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00500-015-1786-2", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1025302830", 
              "https://doi.org/10.1007/s00500-015-1786-2"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.knosys.2015.07.027", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1027641429"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.knosys.2013.12.011", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031370670"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.swevo.2016.06.002", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1031896533"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.amc.2014.02.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032338119"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.knosys.2014.02.021", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036494949"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-3-319-09339-0_68", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1036633905", 
              "https://doi.org/10.1007/978-3-319-09339-0_68"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00521-015-1870-7", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1037907456", 
              "https://doi.org/10.1007/s00521-015-1870-7"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1057/palgrave.jors.2600425", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1039205956", 
              "https://doi.org/10.1057/palgrave.jors.2600425"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.4028/www.scientific.net/amr.756-759.2952", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043230069"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.chemolab.2014.12.007", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1043981518"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1162/evco.2007.15.1.1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1045395868"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.knosys.2011.07.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1048051050"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.knosys.2014.01.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1050851617"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.knosys.2016.09.027", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051115259"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.knosys.2015.08.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1051465147"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1002/9780470400531.eorms0515", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1052451881"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1080/09540091.2013.854735", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053702900"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.eswa.2016.08.039", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1054733983"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tcyb.2016.2554622", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061580312"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tevc.2003.819944", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061604614"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tevc.2005.857610", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061604714"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tevc.2011.2132725", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061605044"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.ins.2017.02.029", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1083825243"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.swevo.2017.06.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1085934603"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.procs.2017.06.010", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091307974"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.procs.2017.06.009", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091308902"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.asoc.2017.08.037", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091337827"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cec.2016.7744219", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1093347874"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cec.2005.1554904", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094384105"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/mhs.1995.494215", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1095205003"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12652-017-0667-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100077440", 
              "https://doi.org/10.1007/s12652-017-0667-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12652-017-0667-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100077440", 
              "https://doi.org/10.1007/s12652-017-0667-1"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s12652-017-0667-1", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1100077440", 
              "https://doi.org/10.1007/s12652-017-0667-1"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2019-04", 
        "datePublishedReg": "2019-04-01", 
        "description": "As a novel population-based optimization algorithm, fruit fly optimization (FFO) algorithm is inspired by the foraging behavior of fruit flies and possesses the advantages of simple search operations and easy implementation. Just like most population-based evolutionary algorithms, the basic FFO also suffers from being trapped in local optima for function optimization due to premature convergence. In this paper, an improved FFO, named IPGS-FFO, is proposed in which two novel strategies are incorporated into the conventional FFO. Specifically, a smell sensitivity parameter together with an inverse power generation mechanism (IPGS) is introduced to enhance local exploitation. Moreover, a dynamic shrinking search radius strategy is incorporated so as to enhance the global exploration over search space by adaptively adjusting the searching area in the problem domain. The statistical performance of FFO, the proposed IPGS-FFO, three state-of-the-art FFO variants, and six metaheuristics are tested on twenty-six well-known unimodal and multimodal benchmark functions with dimension 30, respectively. Experimental results and comparisons show that the proposed IPGS-FFO achieves better performance than three FFO variants and competitive performance against six other meta-heuristics in terms of the solution accuracy and convergence rate.", 
        "genre": "research_article", 
        "id": "sg:pub.10.1007/s11424-018-7250-5", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1053706", 
            "issn": [
              "1009-6124", 
              "1559-7067"
            ], 
            "name": "Journal of Systems Science and Complexity", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "2", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "32"
          }
        ], 
        "name": "An Inverse Power Generation Mechanism Based Fruit Fly Algorithm for Function Optimization", 
        "pagination": "634-656", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "1a9ed22d683abdef792beec0a00e4e845700c07bd7e0cd35c3e4aaaf35abd491"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/s11424-018-7250-5"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1110065333"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1007/s11424-018-7250-5", 
          "https://app.dimensions.ai/details/publication/pub.1110065333"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-11T13:21", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000368_0000000368/records_78972_00000001.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "https://link.springer.com/10.1007%2Fs11424-018-7250-5"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s11424-018-7250-5'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s11424-018-7250-5'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s11424-018-7250-5'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s11424-018-7250-5'


     

    This table displays all metadata directly associated to this object as RDF triples.

    268 TRIPLES      21 PREDICATES      81 URIs      19 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/s11424-018-7250-5 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N66d27ac683fa4a3e9d6255767f0ffe8f
    4 schema:citation sg:pub.10.1007/978-3-319-09339-0_68
    5 sg:pub.10.1007/978-3-319-42291-6_51
    6 sg:pub.10.1007/978-3-319-42291-6_54
    7 sg:pub.10.1007/978-3-642-04944-6_14
    8 sg:pub.10.1007/s00500-015-1786-2
    9 sg:pub.10.1007/s00521-015-1870-7
    10 sg:pub.10.1007/s00521-015-1942-8
    11 sg:pub.10.1007/s10479-011-0894-3
    12 sg:pub.10.1007/s12652-017-0667-1
    13 sg:pub.10.1023/a:1008202821328
    14 sg:pub.10.1023/a:1022602019183
    15 sg:pub.10.1057/palgrave.jors.2600425
    16 https://doi.org/10.1002/9780470400531.eorms0515
    17 https://doi.org/10.1016/0895-7177(93)90204-c
    18 https://doi.org/10.1016/j.amc.2007.09.004
    19 https://doi.org/10.1016/j.amc.2014.02.005
    20 https://doi.org/10.1016/j.amc.2015.07.030
    21 https://doi.org/10.1016/j.asoc.2016.11.023
    22 https://doi.org/10.1016/j.asoc.2017.08.037
    23 https://doi.org/10.1016/j.chemolab.2014.12.007
    24 https://doi.org/10.1016/j.cie.2015.05.022
    25 https://doi.org/10.1016/j.compstruc.2016.11.005
    26 https://doi.org/10.1016/j.cor.2014.10.008
    27 https://doi.org/10.1016/j.eswa.2015.01.048
    28 https://doi.org/10.1016/j.eswa.2016.08.039
    29 https://doi.org/10.1016/j.ins.2015.09.006
    30 https://doi.org/10.1016/j.ins.2017.02.029
    31 https://doi.org/10.1016/j.knosys.2011.07.001
    32 https://doi.org/10.1016/j.knosys.2012.08.015
    33 https://doi.org/10.1016/j.knosys.2013.04.003
    34 https://doi.org/10.1016/j.knosys.2013.12.011
    35 https://doi.org/10.1016/j.knosys.2014.01.010
    36 https://doi.org/10.1016/j.knosys.2014.02.021
    37 https://doi.org/10.1016/j.knosys.2015.07.027
    38 https://doi.org/10.1016/j.knosys.2015.08.010
    39 https://doi.org/10.1016/j.knosys.2015.09.006
    40 https://doi.org/10.1016/j.knosys.2016.09.027
    41 https://doi.org/10.1016/j.procs.2017.06.009
    42 https://doi.org/10.1016/j.procs.2017.06.010
    43 https://doi.org/10.1016/j.swevo.2016.06.002
    44 https://doi.org/10.1016/j.swevo.2017.06.001
    45 https://doi.org/10.1080/00207543.2016.1170226
    46 https://doi.org/10.1080/09540091.2013.854735
    47 https://doi.org/10.1108/k-02-2014-0024
    48 https://doi.org/10.1109/cec.2005.1554904
    49 https://doi.org/10.1109/cec.2016.7744219
    50 https://doi.org/10.1109/mhs.1995.494215
    51 https://doi.org/10.1109/tcyb.2016.2554622
    52 https://doi.org/10.1109/tevc.2003.819944
    53 https://doi.org/10.1109/tevc.2005.857610
    54 https://doi.org/10.1109/tevc.2011.2132725
    55 https://doi.org/10.1155/2013/108768
    56 https://doi.org/10.1162/evco.2007.15.1.1
    57 https://doi.org/10.4028/www.scientific.net/amr.756-759.2952
    58 schema:datePublished 2019-04
    59 schema:datePublishedReg 2019-04-01
    60 schema:description As a novel population-based optimization algorithm, fruit fly optimization (FFO) algorithm is inspired by the foraging behavior of fruit flies and possesses the advantages of simple search operations and easy implementation. Just like most population-based evolutionary algorithms, the basic FFO also suffers from being trapped in local optima for function optimization due to premature convergence. In this paper, an improved FFO, named IPGS-FFO, is proposed in which two novel strategies are incorporated into the conventional FFO. Specifically, a smell sensitivity parameter together with an inverse power generation mechanism (IPGS) is introduced to enhance local exploitation. Moreover, a dynamic shrinking search radius strategy is incorporated so as to enhance the global exploration over search space by adaptively adjusting the searching area in the problem domain. The statistical performance of FFO, the proposed IPGS-FFO, three state-of-the-art FFO variants, and six metaheuristics are tested on twenty-six well-known unimodal and multimodal benchmark functions with dimension 30, respectively. Experimental results and comparisons show that the proposed IPGS-FFO achieves better performance than three FFO variants and competitive performance against six other meta-heuristics in terms of the solution accuracy and convergence rate.
    61 schema:genre research_article
    62 schema:inLanguage en
    63 schema:isAccessibleForFree false
    64 schema:isPartOf N3405a4f4f7c644cba96fe6e1ead531ad
    65 N49251786dde443b99e51519dd11ad36f
    66 sg:journal.1053706
    67 schema:name An Inverse Power Generation Mechanism Based Fruit Fly Algorithm for Function Optimization
    68 schema:pagination 634-656
    69 schema:productId N437bf6ea0ed943d6b0868e059ec79e0f
    70 N9035396174214d2b9194bb8c7324c6ee
    71 Nbe75bb3ac4f846e0bf41a51f1412315c
    72 schema:sameAs https://app.dimensions.ai/details/publication/pub.1110065333
    73 https://doi.org/10.1007/s11424-018-7250-5
    74 schema:sdDatePublished 2019-04-11T13:21
    75 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    76 schema:sdPublisher N3048735d1cf64ebea89518f9043adf87
    77 schema:url https://link.springer.com/10.1007%2Fs11424-018-7250-5
    78 sgo:license sg:explorer/license/
    79 sgo:sdDataset articles
    80 rdf:type schema:ScholarlyArticle
    81 N053d4e9149024ce8936505ea8b53e69e rdf:first N251ad38d12484560a29a8786a6dfae38
    82 rdf:rest rdf:nil
    83 N251ad38d12484560a29a8786a6dfae38 schema:affiliation https://www.grid.ac/institutes/grid.458463.8
    84 schema:familyName Liu
    85 schema:givenName Bo
    86 rdf:type schema:Person
    87 N3048735d1cf64ebea89518f9043adf87 schema:name Springer Nature - SN SciGraph project
    88 rdf:type schema:Organization
    89 N3405a4f4f7c644cba96fe6e1ead531ad schema:volumeNumber 32
    90 rdf:type schema:PublicationVolume
    91 N437bf6ea0ed943d6b0868e059ec79e0f schema:name doi
    92 schema:value 10.1007/s11424-018-7250-5
    93 rdf:type schema:PropertyValue
    94 N49251786dde443b99e51519dd11ad36f schema:issueNumber 2
    95 rdf:type schema:PublicationIssue
    96 N66d27ac683fa4a3e9d6255767f0ffe8f rdf:first sg:person.010077612142.24
    97 rdf:rest N93772166c2dc40e0bf662fa68c3f650a
    98 N868ced3cf6f24cadb5166e3bcea64e9f schema:affiliation https://www.grid.ac/institutes/grid.64939.31
    99 schema:familyName Liu
    100 schema:givenName Ying
    101 rdf:type schema:Person
    102 N9035396174214d2b9194bb8c7324c6ee schema:name dimensions_id
    103 schema:value pub.1110065333
    104 rdf:type schema:PropertyValue
    105 N93772166c2dc40e0bf662fa68c3f650a rdf:first sg:person.010675172542.42
    106 rdf:rest Nbc6431fb1f844f708f9cc163e204b52f
    107 Na5dc3d31d9564fedb5502b28a879e709 schema:affiliation https://www.grid.ac/institutes/grid.412787.f
    108 schema:familyName Ren
    109 schema:givenName Liang
    110 rdf:type schema:Person
    111 Nb2c7605a94ab46ed8cfb508b8f4afe9d rdf:first N868ced3cf6f24cadb5166e3bcea64e9f
    112 rdf:rest N053d4e9149024ce8936505ea8b53e69e
    113 Nbc6431fb1f844f708f9cc163e204b52f rdf:first Na5dc3d31d9564fedb5502b28a879e709
    114 rdf:rest Nb2c7605a94ab46ed8cfb508b8f4afe9d
    115 Nbe75bb3ac4f846e0bf41a51f1412315c schema:name readcube_id
    116 schema:value 1a9ed22d683abdef792beec0a00e4e845700c07bd7e0cd35c3e4aaaf35abd491
    117 rdf:type schema:PropertyValue
    118 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    119 schema:name Information and Computing Sciences
    120 rdf:type schema:DefinedTerm
    121 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    122 schema:name Artificial Intelligence and Image Processing
    123 rdf:type schema:DefinedTerm
    124 sg:journal.1053706 schema:issn 1009-6124
    125 1559-7067
    126 schema:name Journal of Systems Science and Complexity
    127 rdf:type schema:Periodical
    128 sg:person.010077612142.24 schema:affiliation https://www.grid.ac/institutes/grid.412787.f
    129 schema:familyName Liu
    130 schema:givenName Ao
    131 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010077612142.24
    132 rdf:type schema:Person
    133 sg:person.010675172542.42 schema:affiliation https://www.grid.ac/institutes/grid.412787.f
    134 schema:familyName Deng
    135 schema:givenName Xudong
    136 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010675172542.42
    137 rdf:type schema:Person
    138 sg:pub.10.1007/978-3-319-09339-0_68 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036633905
    139 https://doi.org/10.1007/978-3-319-09339-0_68
    140 rdf:type schema:CreativeWork
    141 sg:pub.10.1007/978-3-319-42291-6_51 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011501282
    142 https://doi.org/10.1007/978-3-319-42291-6_51
    143 rdf:type schema:CreativeWork
    144 sg:pub.10.1007/978-3-319-42291-6_54 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014667151
    145 https://doi.org/10.1007/978-3-319-42291-6_54
    146 rdf:type schema:CreativeWork
    147 sg:pub.10.1007/978-3-642-04944-6_14 schema:sameAs https://app.dimensions.ai/details/publication/pub.1003585062
    148 https://doi.org/10.1007/978-3-642-04944-6_14
    149 rdf:type schema:CreativeWork
    150 sg:pub.10.1007/s00500-015-1786-2 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025302830
    151 https://doi.org/10.1007/s00500-015-1786-2
    152 rdf:type schema:CreativeWork
    153 sg:pub.10.1007/s00521-015-1870-7 schema:sameAs https://app.dimensions.ai/details/publication/pub.1037907456
    154 https://doi.org/10.1007/s00521-015-1870-7
    155 rdf:type schema:CreativeWork
    156 sg:pub.10.1007/s00521-015-1942-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019565319
    157 https://doi.org/10.1007/s00521-015-1942-8
    158 rdf:type schema:CreativeWork
    159 sg:pub.10.1007/s10479-011-0894-3 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024832611
    160 https://doi.org/10.1007/s10479-011-0894-3
    161 rdf:type schema:CreativeWork
    162 sg:pub.10.1007/s12652-017-0667-1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1100077440
    163 https://doi.org/10.1007/s12652-017-0667-1
    164 rdf:type schema:CreativeWork
    165 sg:pub.10.1023/a:1008202821328 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012950914
    166 https://doi.org/10.1023/a:1008202821328
    167 rdf:type schema:CreativeWork
    168 sg:pub.10.1023/a:1022602019183 schema:sameAs https://app.dimensions.ai/details/publication/pub.1009899114
    169 https://doi.org/10.1023/a:1022602019183
    170 rdf:type schema:CreativeWork
    171 sg:pub.10.1057/palgrave.jors.2600425 schema:sameAs https://app.dimensions.ai/details/publication/pub.1039205956
    172 https://doi.org/10.1057/palgrave.jors.2600425
    173 rdf:type schema:CreativeWork
    174 https://doi.org/10.1002/9780470400531.eorms0515 schema:sameAs https://app.dimensions.ai/details/publication/pub.1052451881
    175 rdf:type schema:CreativeWork
    176 https://doi.org/10.1016/0895-7177(93)90204-c schema:sameAs https://app.dimensions.ai/details/publication/pub.1005564211
    177 rdf:type schema:CreativeWork
    178 https://doi.org/10.1016/j.amc.2007.09.004 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014039403
    179 rdf:type schema:CreativeWork
    180 https://doi.org/10.1016/j.amc.2014.02.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032338119
    181 rdf:type schema:CreativeWork
    182 https://doi.org/10.1016/j.amc.2015.07.030 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013239196
    183 rdf:type schema:CreativeWork
    184 https://doi.org/10.1016/j.asoc.2016.11.023 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023753641
    185 rdf:type schema:CreativeWork
    186 https://doi.org/10.1016/j.asoc.2017.08.037 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091337827
    187 rdf:type schema:CreativeWork
    188 https://doi.org/10.1016/j.chemolab.2014.12.007 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043981518
    189 rdf:type schema:CreativeWork
    190 https://doi.org/10.1016/j.cie.2015.05.022 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012078768
    191 rdf:type schema:CreativeWork
    192 https://doi.org/10.1016/j.compstruc.2016.11.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015343268
    193 rdf:type schema:CreativeWork
    194 https://doi.org/10.1016/j.cor.2014.10.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1017728181
    195 rdf:type schema:CreativeWork
    196 https://doi.org/10.1016/j.eswa.2015.01.048 schema:sameAs https://app.dimensions.ai/details/publication/pub.1014936911
    197 rdf:type schema:CreativeWork
    198 https://doi.org/10.1016/j.eswa.2016.08.039 schema:sameAs https://app.dimensions.ai/details/publication/pub.1054733983
    199 rdf:type schema:CreativeWork
    200 https://doi.org/10.1016/j.ins.2015.09.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1004480062
    201 rdf:type schema:CreativeWork
    202 https://doi.org/10.1016/j.ins.2017.02.029 schema:sameAs https://app.dimensions.ai/details/publication/pub.1083825243
    203 rdf:type schema:CreativeWork
    204 https://doi.org/10.1016/j.knosys.2011.07.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1048051050
    205 rdf:type schema:CreativeWork
    206 https://doi.org/10.1016/j.knosys.2012.08.015 schema:sameAs https://app.dimensions.ai/details/publication/pub.1007216168
    207 rdf:type schema:CreativeWork
    208 https://doi.org/10.1016/j.knosys.2013.04.003 schema:sameAs https://app.dimensions.ai/details/publication/pub.1025030139
    209 rdf:type schema:CreativeWork
    210 https://doi.org/10.1016/j.knosys.2013.12.011 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031370670
    211 rdf:type schema:CreativeWork
    212 https://doi.org/10.1016/j.knosys.2014.01.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1050851617
    213 rdf:type schema:CreativeWork
    214 https://doi.org/10.1016/j.knosys.2014.02.021 schema:sameAs https://app.dimensions.ai/details/publication/pub.1036494949
    215 rdf:type schema:CreativeWork
    216 https://doi.org/10.1016/j.knosys.2015.07.027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1027641429
    217 rdf:type schema:CreativeWork
    218 https://doi.org/10.1016/j.knosys.2015.08.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051465147
    219 rdf:type schema:CreativeWork
    220 https://doi.org/10.1016/j.knosys.2015.09.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1012647911
    221 rdf:type schema:CreativeWork
    222 https://doi.org/10.1016/j.knosys.2016.09.027 schema:sameAs https://app.dimensions.ai/details/publication/pub.1051115259
    223 rdf:type schema:CreativeWork
    224 https://doi.org/10.1016/j.procs.2017.06.009 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091308902
    225 rdf:type schema:CreativeWork
    226 https://doi.org/10.1016/j.procs.2017.06.010 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091307974
    227 rdf:type schema:CreativeWork
    228 https://doi.org/10.1016/j.swevo.2016.06.002 schema:sameAs https://app.dimensions.ai/details/publication/pub.1031896533
    229 rdf:type schema:CreativeWork
    230 https://doi.org/10.1016/j.swevo.2017.06.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1085934603
    231 rdf:type schema:CreativeWork
    232 https://doi.org/10.1080/00207543.2016.1170226 schema:sameAs https://app.dimensions.ai/details/publication/pub.1000024513
    233 rdf:type schema:CreativeWork
    234 https://doi.org/10.1080/09540091.2013.854735 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053702900
    235 rdf:type schema:CreativeWork
    236 https://doi.org/10.1108/k-02-2014-0024 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020117544
    237 rdf:type schema:CreativeWork
    238 https://doi.org/10.1109/cec.2005.1554904 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094384105
    239 rdf:type schema:CreativeWork
    240 https://doi.org/10.1109/cec.2016.7744219 schema:sameAs https://app.dimensions.ai/details/publication/pub.1093347874
    241 rdf:type schema:CreativeWork
    242 https://doi.org/10.1109/mhs.1995.494215 schema:sameAs https://app.dimensions.ai/details/publication/pub.1095205003
    243 rdf:type schema:CreativeWork
    244 https://doi.org/10.1109/tcyb.2016.2554622 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061580312
    245 rdf:type schema:CreativeWork
    246 https://doi.org/10.1109/tevc.2003.819944 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061604614
    247 rdf:type schema:CreativeWork
    248 https://doi.org/10.1109/tevc.2005.857610 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061604714
    249 rdf:type schema:CreativeWork
    250 https://doi.org/10.1109/tevc.2011.2132725 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061605044
    251 rdf:type schema:CreativeWork
    252 https://doi.org/10.1155/2013/108768 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019623677
    253 rdf:type schema:CreativeWork
    254 https://doi.org/10.1162/evco.2007.15.1.1 schema:sameAs https://app.dimensions.ai/details/publication/pub.1045395868
    255 rdf:type schema:CreativeWork
    256 https://doi.org/10.4028/www.scientific.net/amr.756-759.2952 schema:sameAs https://app.dimensions.ai/details/publication/pub.1043230069
    257 rdf:type schema:CreativeWork
    258 https://www.grid.ac/institutes/grid.412787.f schema:alternateName Wuhan University of Science and Technology
    259 schema:name Center for Service Science and Engineering, Wuhan University of Science and Technology, 430065, Wuhan, China
    260 Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System, 430065, Wuhan, China
    261 School of Management, Wuhan University of Science and Technology, 430065, Wuhan, China
    262 rdf:type schema:Organization
    263 https://www.grid.ac/institutes/grid.458463.8 schema:alternateName Academy of Mathematics and Systems Science
    264 schema:name Academy of Mathematics and Systems Science, Chinese Academy of Sciences, 100190, Beijing, China
    265 rdf:type schema:Organization
    266 https://www.grid.ac/institutes/grid.64939.31 schema:alternateName Beihang University
    267 schema:name School of Economics and Management, Beihang University, 100191, Beijing, China
    268 rdf:type schema:Organization
     




    Preview window. Press ESC to close (or click here)


    ...