Harmony Search Based Algorithms for the Minimum Interference Frequency Assignment Problem View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017

AUTHORS

Yasmine Lahsinat , Dalila Boughaci , Belaid Benhamou

ABSTRACT

The Minimum Interference Frequency Assignment Problem (MI-FAP) is a particularly hard combinatorial optimization problem. It consists in the assignment of a limited number of frequencies to each transceiver of the network without or at least with a low level of interference. In this work, we present an adaptation of the Harmony Search (HS) algorithm to tackle the MI-FAP problem. The results obtained by the adaptation of the classical Harmony Search algorithm are unsatisfactory. We performed a computation testing over some data sets of various sizes picked from public available benchmarks. The experimental results show that the conventional harmony search suffers from its premature convergence and therefore gets stuck in local optima. Even when it succeeds to escape from the local optimum, it does it after a long period of time. This make the process very slow. Due to these unconvincing results, we want to improve the Harmony Search algorithm’s performances. To handle that, we propose some small changes and tricks that we bring to the original Harmony Search algorithm and a hybridization with a local search and the Opposition Based Learning (OPBL) principle. Here, we propose two strategies to improve the performances of the classical harmony search algorithm. We will show that both of them succeeds to enhance the performances of the harmony search in solving the MI-FAP. One of the proposed strategies gives as good results as those of the state of the art for some instances. Nevertheless, the method still need adjustment to be more competitive. More... »

PAGES

179-189

References to SciGraph publications

  • 2011-05. Optimization algorithms for large-scale real-world instances of the frequency assignment problem in SOFT COMPUTING
  • 2011-12. Genetic Tabu search for robust fixed channel assignment under dynamic traffic data in COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
  • 2007-09. Models and solution techniques for frequency assignment problems in ANNALS OF OPERATIONS RESEARCH
  • 1999. Tabu Search for Graph Coloring, T-Colorings and Set T-Colorings in META-HEURISTICS: ADVANCES AND TRENDS IN LOCAL SEARCH PARADIGMS FOR OPTIMIZATION
  • Book

    TITLE

    Harmony Search Algorithm

    ISBN

    978-981-10-3727-6
    978-981-10-3728-3

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-981-10-3728-3_18

    DOI

    http://dx.doi.org/10.1007/978-981-10-3728-3_18

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1074238665


    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": {
              "name": [
                "LRIA-FEI/USTHB Alger Algeria"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Lahsinat", 
            "givenName": "Yasmine", 
            "id": "sg:person.016171600116.36", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016171600116.36"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "name": [
                "LRIA-FEI/USTHB Alger Algeria"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Boughaci", 
            "givenName": "Dalila", 
            "id": "sg:person.016701724165.24", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016701724165.24"
            ], 
            "type": "Person"
          }, 
          {
            "affiliation": {
              "alternateName": "Laboratoire des Sciences de l'Information et des Syst\u00e8mes", 
              "id": "https://www.grid.ac/institutes/grid.462878.7", 
              "name": [
                "Universit\u00e9 Aix-Marseille, LSIS, Domaine universitaire de Saint J\u00e9r\u00f4me Marseille Cedex 20 France"
              ], 
              "type": "Organization"
            }, 
            "familyName": "Benhamou", 
            "givenName": "Belaid", 
            "id": "sg:person.011525467135.35", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011525467135.35"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.1016/j.comcom.2012.10.008", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1011092638"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.cor.2008.08.006", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1013992861"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4615-5775-3_6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015167641", 
              "https://doi.org/10.1007/978-1-4615-5775-3_6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/978-1-4615-5775-3_6", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1015167641", 
              "https://doi.org/10.1007/978-1-4615-5775-3_6"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.engappai.2011.02.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019301992"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.engappai.2011.02.005", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1019301992"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.asoc.2011.10.001", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1023554723"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/j.eswa.2015.01.025", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1024014777"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s00500-010-0653-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028204938", 
              "https://doi.org/10.1007/s00500-010-0653-4"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1145/1276958.1276972", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1028914459"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1016/s0377-2217(99)00254-4", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1029287785"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10589-010-9376-9", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1030237732", 
              "https://doi.org/10.1007/s10589-010-9376-9"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "sg:pub.10.1007/s10479-007-0178-0", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1032339405", 
              "https://doi.org/10.1007/s10479-007-0178-0"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/003754970107600201", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053394783"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1177/003754970107600201", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1053394783"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/25.192382", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061134328"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/proc.1980.11899", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061444670"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tmc.2012.153", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061690869"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/tvt.2003.810976", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1061818213"
            ], 
            "type": "CreativeWork"
          }, 
          {
            "id": "https://doi.org/10.1109/cimca.2005.1631345", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1094682770"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "2017", 
        "datePublishedReg": "2017-01-01", 
        "description": "The Minimum Interference Frequency Assignment Problem (MI-FAP) is a particularly hard combinatorial optimization problem. It consists in the assignment of a limited number of frequencies to each transceiver of the network without or at least with a low level of interference. In this work, we present an adaptation of the Harmony Search (HS) algorithm to tackle the MI-FAP problem. The results obtained by the adaptation of the classical Harmony Search algorithm are unsatisfactory. We performed a computation testing over some data sets of various sizes picked from public available benchmarks. The experimental results show that the conventional harmony search suffers from its premature convergence and therefore gets stuck in local optima. Even when it succeeds to escape from the local optimum, it does it after a long period of time. This make the process very slow. Due to these unconvincing results, we want to improve the Harmony Search algorithm\u2019s performances. To handle that, we propose some small changes and tricks that we bring to the original Harmony Search algorithm and a hybridization with a local search and the Opposition Based Learning (OPBL) principle. Here, we propose two strategies to improve the performances of the classical harmony search algorithm. We will show that both of them succeeds to enhance the performances of the harmony search in solving the MI-FAP. One of the proposed strategies gives as good results as those of the state of the art for some instances. Nevertheless, the method still need adjustment to be more competitive.", 
        "editor": [
          {
            "familyName": "Del Ser", 
            "givenName": "Javier", 
            "type": "Person"
          }
        ], 
        "genre": "chapter", 
        "id": "sg:pub.10.1007/978-981-10-3728-3_18", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": {
          "isbn": [
            "978-981-10-3727-6", 
            "978-981-10-3728-3"
          ], 
          "name": "Harmony Search Algorithm", 
          "type": "Book"
        }, 
        "name": "Harmony Search Based Algorithms for the Minimum Interference Frequency Assignment Problem", 
        "pagination": "179-189", 
        "productId": [
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1007/978-981-10-3728-3_18"
            ]
          }, 
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "9347395894dffc1543a0e1c4b8faf7be19b1fff58d41db74f50cd93735e0a79a"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1074238665"
            ]
          }
        ], 
        "publisher": {
          "location": "Singapore", 
          "name": "Springer Singapore", 
          "type": "Organisation"
        }, 
        "sameAs": [
          "https://doi.org/10.1007/978-981-10-3728-3_18", 
          "https://app.dimensions.ai/details/publication/pub.1074238665"
        ], 
        "sdDataset": "chapters", 
        "sdDatePublished": "2019-04-15T10:45", 
        "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/v90/0000000001_0000000264/records_8659_00000331.jsonl", 
        "type": "Chapter", 
        "url": "http://link.springer.com/10.1007/978-981-10-3728-3_18"
      }
    ]
     

    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.10.1007/978-981-10-3728-3_18'

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

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-981-10-3728-3_18'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-981-10-3728-3_18'

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

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-981-10-3728-3_18'


     

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

    138 TRIPLES      23 PREDICATES      44 URIs      20 LITERALS      8 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1007/978-981-10-3728-3_18 schema:about anzsrc-for:08
    2 anzsrc-for:0801
    3 schema:author N22f9c51cadc340d6bed43b6741124c5c
    4 schema:citation sg:pub.10.1007/978-1-4615-5775-3_6
    5 sg:pub.10.1007/s00500-010-0653-4
    6 sg:pub.10.1007/s10479-007-0178-0
    7 sg:pub.10.1007/s10589-010-9376-9
    8 https://doi.org/10.1016/j.asoc.2011.10.001
    9 https://doi.org/10.1016/j.comcom.2012.10.008
    10 https://doi.org/10.1016/j.cor.2008.08.006
    11 https://doi.org/10.1016/j.engappai.2011.02.005
    12 https://doi.org/10.1016/j.eswa.2015.01.025
    13 https://doi.org/10.1016/s0377-2217(99)00254-4
    14 https://doi.org/10.1109/25.192382
    15 https://doi.org/10.1109/cimca.2005.1631345
    16 https://doi.org/10.1109/proc.1980.11899
    17 https://doi.org/10.1109/tmc.2012.153
    18 https://doi.org/10.1109/tvt.2003.810976
    19 https://doi.org/10.1145/1276958.1276972
    20 https://doi.org/10.1177/003754970107600201
    21 schema:datePublished 2017
    22 schema:datePublishedReg 2017-01-01
    23 schema:description The Minimum Interference Frequency Assignment Problem (MI-FAP) is a particularly hard combinatorial optimization problem. It consists in the assignment of a limited number of frequencies to each transceiver of the network without or at least with a low level of interference. In this work, we present an adaptation of the Harmony Search (HS) algorithm to tackle the MI-FAP problem. The results obtained by the adaptation of the classical Harmony Search algorithm are unsatisfactory. We performed a computation testing over some data sets of various sizes picked from public available benchmarks. The experimental results show that the conventional harmony search suffers from its premature convergence and therefore gets stuck in local optima. Even when it succeeds to escape from the local optimum, it does it after a long period of time. This make the process very slow. Due to these unconvincing results, we want to improve the Harmony Search algorithm’s performances. To handle that, we propose some small changes and tricks that we bring to the original Harmony Search algorithm and a hybridization with a local search and the Opposition Based Learning (OPBL) principle. Here, we propose two strategies to improve the performances of the classical harmony search algorithm. We will show that both of them succeeds to enhance the performances of the harmony search in solving the MI-FAP. One of the proposed strategies gives as good results as those of the state of the art for some instances. Nevertheless, the method still need adjustment to be more competitive.
    24 schema:editor N900a57181b69444dbdce8aefcd3b6115
    25 schema:genre chapter
    26 schema:inLanguage en
    27 schema:isAccessibleForFree false
    28 schema:isPartOf Nc9c803f678c741789c899a528ae53a0a
    29 schema:name Harmony Search Based Algorithms for the Minimum Interference Frequency Assignment Problem
    30 schema:pagination 179-189
    31 schema:productId N2655fa7688a44c188e63b9af8c79706a
    32 N69e4604642e6482ebb570e0eb407579d
    33 Ne3eb2b540dbc44f6a03133736617c06d
    34 schema:publisher N25b4450645f548509ed1d31008316c7c
    35 schema:sameAs https://app.dimensions.ai/details/publication/pub.1074238665
    36 https://doi.org/10.1007/978-981-10-3728-3_18
    37 schema:sdDatePublished 2019-04-15T10:45
    38 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    39 schema:sdPublisher N901db58d4c884bcf91695e80ced86a7f
    40 schema:url http://link.springer.com/10.1007/978-981-10-3728-3_18
    41 sgo:license sg:explorer/license/
    42 sgo:sdDataset chapters
    43 rdf:type schema:Chapter
    44 N22f9c51cadc340d6bed43b6741124c5c rdf:first sg:person.016171600116.36
    45 rdf:rest N854229dc96924c789f8fb88d41b25c80
    46 N25b4450645f548509ed1d31008316c7c schema:location Singapore
    47 schema:name Springer Singapore
    48 rdf:type schema:Organisation
    49 N2655fa7688a44c188e63b9af8c79706a schema:name readcube_id
    50 schema:value 9347395894dffc1543a0e1c4b8faf7be19b1fff58d41db74f50cd93735e0a79a
    51 rdf:type schema:PropertyValue
    52 N69e4604642e6482ebb570e0eb407579d schema:name doi
    53 schema:value 10.1007/978-981-10-3728-3_18
    54 rdf:type schema:PropertyValue
    55 N6f3be6ab341642eabc5d7a47584c5b14 schema:name LRIA-FEI/USTHB Alger Algeria
    56 rdf:type schema:Organization
    57 N854229dc96924c789f8fb88d41b25c80 rdf:first sg:person.016701724165.24
    58 rdf:rest Nec0644ab0f7b41a29d7fae29398ee220
    59 N900a57181b69444dbdce8aefcd3b6115 rdf:first Nd76bea322aea4554a4c57db110e66bba
    60 rdf:rest rdf:nil
    61 N901db58d4c884bcf91695e80ced86a7f schema:name Springer Nature - SN SciGraph project
    62 rdf:type schema:Organization
    63 Nc9c803f678c741789c899a528ae53a0a schema:isbn 978-981-10-3727-6
    64 978-981-10-3728-3
    65 schema:name Harmony Search Algorithm
    66 rdf:type schema:Book
    67 Nd76bea322aea4554a4c57db110e66bba schema:familyName Del Ser
    68 schema:givenName Javier
    69 rdf:type schema:Person
    70 Ne3eb2b540dbc44f6a03133736617c06d schema:name dimensions_id
    71 schema:value pub.1074238665
    72 rdf:type schema:PropertyValue
    73 Nec0644ab0f7b41a29d7fae29398ee220 rdf:first sg:person.011525467135.35
    74 rdf:rest rdf:nil
    75 Nf4e511627614485cb6794f9d781f3e45 schema:name LRIA-FEI/USTHB Alger Algeria
    76 rdf:type schema:Organization
    77 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
    78 schema:name Information and Computing Sciences
    79 rdf:type schema:DefinedTerm
    80 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
    81 schema:name Artificial Intelligence and Image Processing
    82 rdf:type schema:DefinedTerm
    83 sg:person.011525467135.35 schema:affiliation https://www.grid.ac/institutes/grid.462878.7
    84 schema:familyName Benhamou
    85 schema:givenName Belaid
    86 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011525467135.35
    87 rdf:type schema:Person
    88 sg:person.016171600116.36 schema:affiliation Nf4e511627614485cb6794f9d781f3e45
    89 schema:familyName Lahsinat
    90 schema:givenName Yasmine
    91 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016171600116.36
    92 rdf:type schema:Person
    93 sg:person.016701724165.24 schema:affiliation N6f3be6ab341642eabc5d7a47584c5b14
    94 schema:familyName Boughaci
    95 schema:givenName Dalila
    96 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016701724165.24
    97 rdf:type schema:Person
    98 sg:pub.10.1007/978-1-4615-5775-3_6 schema:sameAs https://app.dimensions.ai/details/publication/pub.1015167641
    99 https://doi.org/10.1007/978-1-4615-5775-3_6
    100 rdf:type schema:CreativeWork
    101 sg:pub.10.1007/s00500-010-0653-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028204938
    102 https://doi.org/10.1007/s00500-010-0653-4
    103 rdf:type schema:CreativeWork
    104 sg:pub.10.1007/s10479-007-0178-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1032339405
    105 https://doi.org/10.1007/s10479-007-0178-0
    106 rdf:type schema:CreativeWork
    107 sg:pub.10.1007/s10589-010-9376-9 schema:sameAs https://app.dimensions.ai/details/publication/pub.1030237732
    108 https://doi.org/10.1007/s10589-010-9376-9
    109 rdf:type schema:CreativeWork
    110 https://doi.org/10.1016/j.asoc.2011.10.001 schema:sameAs https://app.dimensions.ai/details/publication/pub.1023554723
    111 rdf:type schema:CreativeWork
    112 https://doi.org/10.1016/j.comcom.2012.10.008 schema:sameAs https://app.dimensions.ai/details/publication/pub.1011092638
    113 rdf:type schema:CreativeWork
    114 https://doi.org/10.1016/j.cor.2008.08.006 schema:sameAs https://app.dimensions.ai/details/publication/pub.1013992861
    115 rdf:type schema:CreativeWork
    116 https://doi.org/10.1016/j.engappai.2011.02.005 schema:sameAs https://app.dimensions.ai/details/publication/pub.1019301992
    117 rdf:type schema:CreativeWork
    118 https://doi.org/10.1016/j.eswa.2015.01.025 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024014777
    119 rdf:type schema:CreativeWork
    120 https://doi.org/10.1016/s0377-2217(99)00254-4 schema:sameAs https://app.dimensions.ai/details/publication/pub.1029287785
    121 rdf:type schema:CreativeWork
    122 https://doi.org/10.1109/25.192382 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061134328
    123 rdf:type schema:CreativeWork
    124 https://doi.org/10.1109/cimca.2005.1631345 schema:sameAs https://app.dimensions.ai/details/publication/pub.1094682770
    125 rdf:type schema:CreativeWork
    126 https://doi.org/10.1109/proc.1980.11899 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061444670
    127 rdf:type schema:CreativeWork
    128 https://doi.org/10.1109/tmc.2012.153 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061690869
    129 rdf:type schema:CreativeWork
    130 https://doi.org/10.1109/tvt.2003.810976 schema:sameAs https://app.dimensions.ai/details/publication/pub.1061818213
    131 rdf:type schema:CreativeWork
    132 https://doi.org/10.1145/1276958.1276972 schema:sameAs https://app.dimensions.ai/details/publication/pub.1028914459
    133 rdf:type schema:CreativeWork
    134 https://doi.org/10.1177/003754970107600201 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053394783
    135 rdf:type schema:CreativeWork
    136 https://www.grid.ac/institutes/grid.462878.7 schema:alternateName Laboratoire des Sciences de l'Information et des Systèmes
    137 schema:name Université Aix-Marseille, LSIS, Domaine universitaire de Saint Jérôme Marseille Cedex 20 France
    138 rdf:type schema:Organization
     




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


    ...