Sensitivity Analysis of ACO Start Strategies for Subset Problems View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2011

AUTHORS

Stefka Fidanova , Pencho Marinov , Krassimir Atanassov

ABSTRACT

Ant Colony Optimization (ACO) has been used successfully to solve hard combinatorial optimization problems. This metaheuristic method is inspired by the foraging behavior of ant colonies, which manage to establish the shortest routes to feeding sources and back. On this work we use estimation of start nodes with respect to the quality of the solution. Various start strategies are offered. Sensitivity analysis of the algorithm behavior according strategy parameters is made. Our ideas is applied on Multiple Knapsack Problem (MKP) like a representative of the subset problems. More... »

PAGES

256-263

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-18466-6_30

DOI

http://dx.doi.org/10.1007/978-3-642-18466-6_30

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1046127839


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/2008/01", 
        "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": "IPP, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl.25A, 1113, Sofia, Bulgaria", 
          "id": "http://www.grid.ac/institutes/grid.424859.6", 
          "name": [
            "IPP, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl.25A, 1113, Sofia, Bulgaria"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fidanova", 
        "givenName": "Stefka", 
        "id": "sg:person.011173106320.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011173106320.18"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "IPP, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl.25A, 1113, Sofia, Bulgaria", 
          "id": "http://www.grid.ac/institutes/grid.424859.6", 
          "name": [
            "IPP, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl.25A, 1113, Sofia, Bulgaria"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Marinov", 
        "givenName": "Pencho", 
        "id": "sg:person.010037302031.75", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010037302031.75"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "CLBME, Bulgarian Academy of Science, Acad. G. Bonchev str, bl 105, 1113, Sofia, Bulgaria", 
          "id": "http://www.grid.ac/institutes/grid.410344.6", 
          "name": [
            "CLBME, Bulgarian Academy of Science, Acad. G. Bonchev str, bl 105, 1113, Sofia, Bulgaria"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Atanassov", 
        "givenName": "Krassimir", 
        "id": "sg:person.013707162366.18", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013707162366.18"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2011", 
    "datePublishedReg": "2011-01-01", 
    "description": "Ant Colony Optimization (ACO) has been used successfully to solve hard combinatorial optimization problems. This metaheuristic method is inspired by the foraging behavior of ant colonies, which manage to establish the shortest routes to feeding sources and back. On this work we use estimation of start nodes with respect to the quality of the solution. Various start strategies are offered. Sensitivity analysis of the algorithm behavior according strategy parameters is made. Our ideas is applied on Multiple Knapsack Problem (MKP) like a representative of the subset problems.", 
    "editor": [
      {
        "familyName": "Dimov", 
        "givenName": "Ivan", 
        "type": "Person"
      }, 
      {
        "familyName": "Dimova", 
        "givenName": "Stefka", 
        "type": "Person"
      }, 
      {
        "familyName": "Kolkovska", 
        "givenName": "Natalia", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-18466-6_30", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-642-18465-9", 
        "978-3-642-18466-6"
      ], 
      "name": "Numerical Methods and Applications", 
      "type": "Book"
    }, 
    "keywords": [
      "Ant Colony Optimization", 
      "multiple knapsack problem", 
      "subset problem", 
      "hard combinatorial optimization problems", 
      "combinatorial optimization problems", 
      "optimization problem", 
      "metaheuristic methods", 
      "colony optimization", 
      "knapsack problem", 
      "start strategy", 
      "algorithm behavior", 
      "sensitivity analysis", 
      "strategy parameters", 
      "problem", 
      "ant colonies", 
      "shortest route", 
      "optimization", 
      "estimation", 
      "solution", 
      "parameters", 
      "behavior", 
      "feeding sources", 
      "nodes", 
      "idea", 
      "respect", 
      "analysis", 
      "work", 
      "foraging behavior", 
      "strategies", 
      "source", 
      "quality", 
      "route", 
      "representatives", 
      "colonies", 
      "method", 
      "ACO Start Strategies"
    ], 
    "name": "Sensitivity Analysis of ACO Start Strategies for Subset Problems", 
    "pagination": "256-263", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1046127839"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-18466-6_30"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-18466-6_30", 
      "https://app.dimensions.ai/details/publication/pub.1046127839"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-01-01T19:26", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/chapter/chapter_52.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-642-18466-6_30"
  }
]
 

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.10.1007/978-3-642-18466-6_30'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-18466-6_30'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-18466-6_30'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-18466-6_30'


 

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

123 TRIPLES      23 PREDICATES      62 URIs      55 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-18466-6_30 schema:about anzsrc-for:01
2 anzsrc-for:0103
3 schema:author N3d55757fff6a452a87b5d4e9478d7be6
4 schema:datePublished 2011
5 schema:datePublishedReg 2011-01-01
6 schema:description Ant Colony Optimization (ACO) has been used successfully to solve hard combinatorial optimization problems. This metaheuristic method is inspired by the foraging behavior of ant colonies, which manage to establish the shortest routes to feeding sources and back. On this work we use estimation of start nodes with respect to the quality of the solution. Various start strategies are offered. Sensitivity analysis of the algorithm behavior according strategy parameters is made. Our ideas is applied on Multiple Knapsack Problem (MKP) like a representative of the subset problems.
7 schema:editor N8bb5edca8f404a9d99160f86291f76f0
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf N696fd9d33f4c49e1ae55773914041fb5
12 schema:keywords ACO Start Strategies
13 Ant Colony Optimization
14 algorithm behavior
15 analysis
16 ant colonies
17 behavior
18 colonies
19 colony optimization
20 combinatorial optimization problems
21 estimation
22 feeding sources
23 foraging behavior
24 hard combinatorial optimization problems
25 idea
26 knapsack problem
27 metaheuristic methods
28 method
29 multiple knapsack problem
30 nodes
31 optimization
32 optimization problem
33 parameters
34 problem
35 quality
36 representatives
37 respect
38 route
39 sensitivity analysis
40 shortest route
41 solution
42 source
43 start strategy
44 strategies
45 strategy parameters
46 subset problem
47 work
48 schema:name Sensitivity Analysis of ACO Start Strategies for Subset Problems
49 schema:pagination 256-263
50 schema:productId N1fbe0f6d13a54681aaea0e9ef8d53669
51 N77e3669c5ceb4262aa6b096138879588
52 schema:publisher N2bc7216c77cd466abe8c1ac6bbf5c4b3
53 schema:sameAs https://app.dimensions.ai/details/publication/pub.1046127839
54 https://doi.org/10.1007/978-3-642-18466-6_30
55 schema:sdDatePublished 2022-01-01T19:26
56 schema:sdLicense https://scigraph.springernature.com/explorer/license/
57 schema:sdPublisher Nfe634dc688304dbb8c63bb6eac22f470
58 schema:url https://doi.org/10.1007/978-3-642-18466-6_30
59 sgo:license sg:explorer/license/
60 sgo:sdDataset chapters
61 rdf:type schema:Chapter
62 N0abc3768e4014fe9bdb0c3e32191a2ef schema:familyName Dimov
63 schema:givenName Ivan
64 rdf:type schema:Person
65 N1fbe0f6d13a54681aaea0e9ef8d53669 schema:name doi
66 schema:value 10.1007/978-3-642-18466-6_30
67 rdf:type schema:PropertyValue
68 N2bc7216c77cd466abe8c1ac6bbf5c4b3 schema:name Springer Nature
69 rdf:type schema:Organisation
70 N3d55757fff6a452a87b5d4e9478d7be6 rdf:first sg:person.011173106320.18
71 rdf:rest Ne1906ca5d29640a28360774d713225c9
72 N458d97da408f45978f058bef48e16656 schema:familyName Dimova
73 schema:givenName Stefka
74 rdf:type schema:Person
75 N696fd9d33f4c49e1ae55773914041fb5 schema:isbn 978-3-642-18465-9
76 978-3-642-18466-6
77 schema:name Numerical Methods and Applications
78 rdf:type schema:Book
79 N6ae52e1c70dc499db954209255a796ed rdf:first sg:person.013707162366.18
80 rdf:rest rdf:nil
81 N77e3669c5ceb4262aa6b096138879588 schema:name dimensions_id
82 schema:value pub.1046127839
83 rdf:type schema:PropertyValue
84 N7840e9ad71ff4810a80439b93c65083f schema:familyName Kolkovska
85 schema:givenName Natalia
86 rdf:type schema:Person
87 N8bb5edca8f404a9d99160f86291f76f0 rdf:first N0abc3768e4014fe9bdb0c3e32191a2ef
88 rdf:rest Neb9c8463301a49e4a71d5f0a244f45ad
89 Ne1906ca5d29640a28360774d713225c9 rdf:first sg:person.010037302031.75
90 rdf:rest N6ae52e1c70dc499db954209255a796ed
91 Ne717e48dfd4342af83315bd12f3fda6c rdf:first N7840e9ad71ff4810a80439b93c65083f
92 rdf:rest rdf:nil
93 Neb9c8463301a49e4a71d5f0a244f45ad rdf:first N458d97da408f45978f058bef48e16656
94 rdf:rest Ne717e48dfd4342af83315bd12f3fda6c
95 Nfe634dc688304dbb8c63bb6eac22f470 schema:name Springer Nature - SN SciGraph project
96 rdf:type schema:Organization
97 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
98 schema:name Mathematical Sciences
99 rdf:type schema:DefinedTerm
100 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
101 schema:name Numerical and Computational Mathematics
102 rdf:type schema:DefinedTerm
103 sg:person.010037302031.75 schema:affiliation grid-institutes:grid.424859.6
104 schema:familyName Marinov
105 schema:givenName Pencho
106 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.010037302031.75
107 rdf:type schema:Person
108 sg:person.011173106320.18 schema:affiliation grid-institutes:grid.424859.6
109 schema:familyName Fidanova
110 schema:givenName Stefka
111 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011173106320.18
112 rdf:type schema:Person
113 sg:person.013707162366.18 schema:affiliation grid-institutes:grid.410344.6
114 schema:familyName Atanassov
115 schema:givenName Krassimir
116 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013707162366.18
117 rdf:type schema:Person
118 grid-institutes:grid.410344.6 schema:alternateName CLBME, Bulgarian Academy of Science, Acad. G. Bonchev str, bl 105, 1113, Sofia, Bulgaria
119 schema:name CLBME, Bulgarian Academy of Science, Acad. G. Bonchev str, bl 105, 1113, Sofia, Bulgaria
120 rdf:type schema:Organization
121 grid-institutes:grid.424859.6 schema:alternateName IPP, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl.25A, 1113, Sofia, Bulgaria
122 schema:name IPP, Bulgarian Academy of Sciences, Acad. G. Bonchev str. bl.25A, 1113, Sofia, Bulgaria
123 rdf:type schema:Organization
 




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


...