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/2008/01", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Mathematical Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0103", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Numerical and Computational Mathematics", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "The University of Electro-Communications, 1-5-1 Chofugaoka, 182-8585, Chofu, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.266298.1", 
          "name": [
            "The University of Electro-Communications, 1-5-1 Chofugaoka, 182-8585, Chofu, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Sato", 
        "givenName": "Hiroyuki", 
        "id": "sg:person.07750750604.05", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07750750604.05"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2014", 
    "datePublishedReg": "2014-01-01", 
    "description": "MOEA/D, a representative multi-objective evolutionary algorithm, decomposes a multi-objective optimization problem into a number of single objective optimization problems and tries to approximate Pareto front by simultaneously optimizing each of these single objective problems. MOEA/D has several options to calculate a scalar value from multiple objective function values of a solution. In many-objective optimization problems including four or more objective functions, MOEA/D using the weighted sum scalarizing function achieves high search performance. However, the weighted sum has a serious problem that the entire concave Pareto front cannot be approximated. To overcome this problem of the weighted sum based MOEA/D, in this work we propose a method to adaptively determine update ranges of solutions in the framework of MOEA/D. The experimental results show that the weighted sum based MOEA/D using the proposed solution update method can approximate the entire concave Pareto front and improve the search performance.", 
    "editor": [
      {
        "familyName": "Dick", 
        "givenName": "Grant", 
        "type": "Person"
      }, 
      {
        "familyName": "Browne", 
        "givenName": "Will N.", 
        "type": "Person"
      }, 
      {
        "familyName": "Whigham", 
        "givenName": "Peter", 
        "type": "Person"
      }, 
      {
        "familyName": "Zhang", 
        "givenName": "Mengjie", 
        "type": "Person"
      }, 
      {
        "familyName": "Bui", 
        "givenName": "Lam Thu", 
        "type": "Person"
      }, 
      {
        "familyName": "Ishibuchi", 
        "givenName": "Hisao", 
        "type": "Person"
      }, 
      {
        "familyName": "Jin", 
        "givenName": "Yaochu", 
        "type": "Person"
      }, 
      {
        "familyName": "Li", 
        "givenName": "Xiaodong", 
        "type": "Person"
      }, 
      {
        "familyName": "Shi", 
        "givenName": "Yuhui", 
        "type": "Person"
      }, 
      {
        "familyName": "Singh", 
        "givenName": "Pramod", 
        "type": "Person"
      }, 
      {
        "familyName": "Tan", 
        "givenName": "Kay Chen", 
        "type": "Person"
      }, 
      {
        "familyName": "Tang", 
        "givenName": "Ke", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-13563-2_24", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-13562-5", 
        "978-3-319-13563-2"
      ], 
      "name": "Simulated Evolution and Learning", 
      "type": "Book"
    }, 
    "keywords": [
      "MOEA/D", 
      "objective optimization problems", 
      "optimization problem", 
      "Pareto front", 
      "concave Pareto fronts", 
      "multi-objective optimization problem", 
      "single-objective optimization problem", 
      "multi-objective evolutionary algorithm", 
      "solution update method", 
      "weighted sum", 
      "representative multi-objective evolutionary algorithms", 
      "single-objective problem", 
      "more objective functions", 
      "objective function value", 
      "MOEA/D.", 
      "high search performance", 
      "objective problems", 
      "objective optimization", 
      "objective function", 
      "evolutionary algorithm", 
      "function values", 
      "update method", 
      "search performance", 
      "scalar values", 
      "sum", 
      "problem", 
      "solution", 
      "experimental results", 
      "Pareto", 
      "optimization", 
      "algorithm", 
      "front", 
      "function", 
      "performance", 
      "Multi", 
      "framework", 
      "values", 
      "range", 
      "number", 
      "work", 
      "results", 
      "D.", 
      "serious problem", 
      "options", 
      "method"
    ], 
    "name": "Adaptive Update Range of Solutions in MOEA/D for Multi and Many-Objective Optimization", 
    "pagination": "274-286", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1016612283"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-13563-2_24"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-13563-2_24", 
      "https://app.dimensions.ai/details/publication/pub.1016612283"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-05-10T10:39", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220509/entities/gbq_results/chapter/chapter_156.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-319-13563-2_24"
  }
]

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

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

160 TRIPLES      23 PREDICATES      71 URIs      64 LITERALS      7 BLANK NODES