Ant Colony Optimization Application to GPS Surveying Problems: InterCriteria Analysis View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2017-09-27

AUTHORS

Stefka Fidanova , Vassia Atanassova , Olympia Roeva

ABSTRACT

Ant Colony Optimization (ACO) has been used successfully to solve hard combinatorial optimization problems. This metaheuristics method is inspired by the foraging behavior of ant colonies, which manage to establish the shortest routes between their colonies to feeding sources and back. In this paper, ACO algorithms are developed to provide near-optimal solutions for Global Positioning System surveying problem (GSP). In designing Global Positioning System (GPS) surveying network, a given set of earth points must be observed consecutively (schedule). The cost of the schedule is the sum of the time needed to go from one point to another. The problem is to search for the best order in which this observation is executed, minimizing the cost of the schedule. We apply InterCriteria Analysis (ICrA) on the achieved results. Based on ICrA we examine some relations between considered GSPs and ACO algorithm performance. More... »

PAGES

251-264

Book

TITLE

Uncertainty and Imprecision in Decision Making and Decision Support: Cross-Fertilization, New Models and Applications

ISBN

978-3-319-65544-4
978-3-319-65545-1

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-65545-1_23

DOI

http://dx.doi.org/10.1007/978-3-319-65545-1_23

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1092033302


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/2008/09", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Engineering", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0909", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Geomatic Engineering", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Institute of Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria", 
          "id": "http://www.grid.ac/institutes/grid.410344.6", 
          "name": [
            "Institute of Information and Communication Technology, Bulgarian Academy of Sciences, 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": "Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria", 
          "id": "http://www.grid.ac/institutes/grid.493309.4", 
          "name": [
            "Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Atanassova", 
        "givenName": "Vassia", 
        "id": "sg:person.013076544445.17", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013076544445.17"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria", 
          "id": "http://www.grid.ac/institutes/grid.493309.4", 
          "name": [
            "Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Roeva", 
        "givenName": "Olympia", 
        "id": "sg:person.015745057111.08", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015745057111.08"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2017-09-27", 
    "datePublishedReg": "2017-09-27", 
    "description": "Ant Colony Optimization (ACO) has been used successfully to solve hard combinatorial optimization problems. This metaheuristics method is inspired by the foraging behavior of ant colonies, which manage to establish the shortest routes between their colonies to feeding sources and back. In this paper, ACO algorithms are developed to provide near-optimal solutions for Global Positioning System surveying problem (GSP). In designing Global Positioning System (GPS) surveying network, a given set of earth points must be observed consecutively (schedule). The cost of the schedule is the sum of the time needed to go from one point to another. The problem is to search for the best order in which this observation is executed, minimizing the cost of the schedule. We apply InterCriteria Analysis (ICrA) on the achieved results. Based on ICrA we examine some relations between considered GSPs and ACO algorithm performance.", 
    "editor": [
      {
        "familyName": "Atanassov", 
        "givenName": "Krassimir T.", 
        "type": "Person"
      }, 
      {
        "familyName": "Kacprzyk", 
        "givenName": "Janusz", 
        "type": "Person"
      }, 
      {
        "familyName": "Ka\u0142uszko", 
        "givenName": "Andrzej", 
        "type": "Person"
      }, 
      {
        "familyName": "Krawczak", 
        "givenName": "Maciej", 
        "type": "Person"
      }, 
      {
        "familyName": "Owsi\u0144ski", 
        "givenName": "Jan", 
        "type": "Person"
      }, 
      {
        "familyName": "Sotirov", 
        "givenName": "Sotir", 
        "type": "Person"
      }, 
      {
        "familyName": "Sotirova", 
        "givenName": "Evdokia", 
        "type": "Person"
      }, 
      {
        "familyName": "Szmidt", 
        "givenName": "Eulalia", 
        "type": "Person"
      }, 
      {
        "familyName": "Zadro\u017cny", 
        "givenName": "S\u0142awomir", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-65545-1_23", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-65544-4", 
        "978-3-319-65545-1"
      ], 
      "name": "Uncertainty and Imprecision in Decision Making and Decision Support: Cross-Fertilization, New Models and Applications", 
      "type": "Book"
    }, 
    "keywords": [
      "Global Positioning System (GPS) surveying networks", 
      "ant colony optimization application", 
      "surveying network", 
      "earth points", 
      "optimization applications", 
      "surveying problems", 
      "ACO algorithm performance", 
      "Ant Colony Optimization", 
      "optimization problem", 
      "near optimal solution", 
      "metaheuristic methods", 
      "cost", 
      "algorithm performance", 
      "optimization", 
      "performance", 
      "problem", 
      "colony optimization", 
      "applications", 
      "feeding sources", 
      "solution", 
      "behavior", 
      "point", 
      "shortest route", 
      "method", 
      "good order", 
      "algorithm", 
      "order", 
      "route", 
      "analysis", 
      "ACO algorithm", 
      "source", 
      "network", 
      "results", 
      "schedule", 
      "time", 
      "combinatorial optimization problems", 
      "observations", 
      "InterCriteria Analysis", 
      "set", 
      "sum", 
      "ant colonies", 
      "hard combinatorial optimization problems", 
      "ICrA", 
      "relation", 
      "colonies", 
      "foraging behavior", 
      "paper"
    ], 
    "name": "Ant Colony Optimization Application to GPS Surveying Problems: InterCriteria Analysis", 
    "pagination": "251-264", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1092033302"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-65545-1_23"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-65545-1_23", 
      "https://app.dimensions.ai/details/publication/pub.1092033302"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-05-10T10:54", 
    "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_460.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-319-65545-1_23"
  }
]
 

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.10.1007/978-3-319-65545-1_23'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-65545-1_23'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-65545-1_23'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-65545-1_23'


 

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

164 TRIPLES      23 PREDICATES      72 URIs      65 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-65545-1_23 schema:about anzsrc-for:09
2 anzsrc-for:0909
3 schema:author Nece9427dcddb45358faa989160c8474c
4 schema:datePublished 2017-09-27
5 schema:datePublishedReg 2017-09-27
6 schema:description Ant Colony Optimization (ACO) has been used successfully to solve hard combinatorial optimization problems. This metaheuristics method is inspired by the foraging behavior of ant colonies, which manage to establish the shortest routes between their colonies to feeding sources and back. In this paper, ACO algorithms are developed to provide near-optimal solutions for Global Positioning System surveying problem (GSP). In designing Global Positioning System (GPS) surveying network, a given set of earth points must be observed consecutively (schedule). The cost of the schedule is the sum of the time needed to go from one point to another. The problem is to search for the best order in which this observation is executed, minimizing the cost of the schedule. We apply InterCriteria Analysis (ICrA) on the achieved results. Based on ICrA we examine some relations between considered GSPs and ACO algorithm performance.
7 schema:editor Ne1489ccd72b145f1a78c69322d7e846e
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf N8de8fe2f404a47e2a599280890859f61
12 schema:keywords ACO algorithm
13 ACO algorithm performance
14 Ant Colony Optimization
15 Global Positioning System (GPS) surveying networks
16 ICrA
17 InterCriteria Analysis
18 algorithm
19 algorithm performance
20 analysis
21 ant colonies
22 ant colony optimization application
23 applications
24 behavior
25 colonies
26 colony optimization
27 combinatorial optimization problems
28 cost
29 earth points
30 feeding sources
31 foraging behavior
32 good order
33 hard combinatorial optimization problems
34 metaheuristic methods
35 method
36 near optimal solution
37 network
38 observations
39 optimization
40 optimization applications
41 optimization problem
42 order
43 paper
44 performance
45 point
46 problem
47 relation
48 results
49 route
50 schedule
51 set
52 shortest route
53 solution
54 source
55 sum
56 surveying network
57 surveying problems
58 time
59 schema:name Ant Colony Optimization Application to GPS Surveying Problems: InterCriteria Analysis
60 schema:pagination 251-264
61 schema:productId N4ad4b10cafa84187970009af194b820a
62 Ncd8c4ba9cc45414cb52b9fe8f44447be
63 schema:publisher N86bcf0ea51f8402e923693b3b8ac6987
64 schema:sameAs https://app.dimensions.ai/details/publication/pub.1092033302
65 https://doi.org/10.1007/978-3-319-65545-1_23
66 schema:sdDatePublished 2022-05-10T10:54
67 schema:sdLicense https://scigraph.springernature.com/explorer/license/
68 schema:sdPublisher Nb56c1c0c413f46b2aab344a700e793b1
69 schema:url https://doi.org/10.1007/978-3-319-65545-1_23
70 sgo:license sg:explorer/license/
71 sgo:sdDataset chapters
72 rdf:type schema:Chapter
73 N16149b6424a14b79ba1f7a67a07d3135 schema:familyName Szmidt
74 schema:givenName Eulalia
75 rdf:type schema:Person
76 N189963911bc94206a5477ac2c881208e rdf:first N5dd5e916dd354e17ad497623c82e50a1
77 rdf:rest N913694ace896433db14576b94979c972
78 N38a5a687768a48b28e4e3a468737f210 schema:familyName Atanassov
79 schema:givenName Krassimir T.
80 rdf:type schema:Person
81 N4ad4b10cafa84187970009af194b820a schema:name doi
82 schema:value 10.1007/978-3-319-65545-1_23
83 rdf:type schema:PropertyValue
84 N5829b0efdc2e491ca3f8d2046021dc52 schema:familyName Sotirova
85 schema:givenName Evdokia
86 rdf:type schema:Person
87 N5dd5e916dd354e17ad497623c82e50a1 schema:familyName Kałuszko
88 schema:givenName Andrzej
89 rdf:type schema:Person
90 N5e26e0d35df14068abf13f3c1baff965 rdf:first N5829b0efdc2e491ca3f8d2046021dc52
91 rdf:rest Ne73e108017dd4b7b94ce365d2b7bd385
92 N7e490b39b9404973bceeee5d841ca9dd schema:familyName Zadrożny
93 schema:givenName Sławomir
94 rdf:type schema:Person
95 N86bcf0ea51f8402e923693b3b8ac6987 schema:name Springer Nature
96 rdf:type schema:Organisation
97 N8de8fe2f404a47e2a599280890859f61 schema:isbn 978-3-319-65544-4
98 978-3-319-65545-1
99 schema:name Uncertainty and Imprecision in Decision Making and Decision Support: Cross-Fertilization, New Models and Applications
100 rdf:type schema:Book
101 N913694ace896433db14576b94979c972 rdf:first Ne865dab1839b4c648dc81aaf8658587a
102 rdf:rest Nf8489a4ea8cd4b129c792f844ca72aef
103 Naf44a3e8d919449f903ef947e3f676f6 rdf:first N7e490b39b9404973bceeee5d841ca9dd
104 rdf:rest rdf:nil
105 Nb56c1c0c413f46b2aab344a700e793b1 schema:name Springer Nature - SN SciGraph project
106 rdf:type schema:Organization
107 Nb8f812fd6a364b9caee91a088e0da5ad schema:familyName Sotirov
108 schema:givenName Sotir
109 rdf:type schema:Person
110 Ncd8c4ba9cc45414cb52b9fe8f44447be schema:name dimensions_id
111 schema:value pub.1092033302
112 rdf:type schema:PropertyValue
113 Ndcacc075657046b2a94b2b7e774872e0 rdf:first sg:person.015745057111.08
114 rdf:rest rdf:nil
115 Ne1109c8fd57f49af897cdb5d970c7050 rdf:first sg:person.013076544445.17
116 rdf:rest Ndcacc075657046b2a94b2b7e774872e0
117 Ne1489ccd72b145f1a78c69322d7e846e rdf:first N38a5a687768a48b28e4e3a468737f210
118 rdf:rest Ne20f65bfdd964a3ab9cedf18dee18b64
119 Ne20f65bfdd964a3ab9cedf18dee18b64 rdf:first Nf501c9770b1c4e9da5cefe29c6d58908
120 rdf:rest N189963911bc94206a5477ac2c881208e
121 Ne73e108017dd4b7b94ce365d2b7bd385 rdf:first N16149b6424a14b79ba1f7a67a07d3135
122 rdf:rest Naf44a3e8d919449f903ef947e3f676f6
123 Ne865dab1839b4c648dc81aaf8658587a schema:familyName Krawczak
124 schema:givenName Maciej
125 rdf:type schema:Person
126 Nece9427dcddb45358faa989160c8474c rdf:first sg:person.011173106320.18
127 rdf:rest Ne1109c8fd57f49af897cdb5d970c7050
128 Nf501c9770b1c4e9da5cefe29c6d58908 schema:familyName Kacprzyk
129 schema:givenName Janusz
130 rdf:type schema:Person
131 Nf65b259e604b491ab9cc1287fb2fcd56 schema:familyName Owsiński
132 schema:givenName Jan
133 rdf:type schema:Person
134 Nf8489a4ea8cd4b129c792f844ca72aef rdf:first Nf65b259e604b491ab9cc1287fb2fcd56
135 rdf:rest Nfe0044e9bd564472bb704c5f0677ec17
136 Nfe0044e9bd564472bb704c5f0677ec17 rdf:first Nb8f812fd6a364b9caee91a088e0da5ad
137 rdf:rest N5e26e0d35df14068abf13f3c1baff965
138 anzsrc-for:09 schema:inDefinedTermSet anzsrc-for:
139 schema:name Engineering
140 rdf:type schema:DefinedTerm
141 anzsrc-for:0909 schema:inDefinedTermSet anzsrc-for:
142 schema:name Geomatic Engineering
143 rdf:type schema:DefinedTerm
144 sg:person.011173106320.18 schema:affiliation grid-institutes:grid.410344.6
145 schema:familyName Fidanova
146 schema:givenName Stefka
147 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011173106320.18
148 rdf:type schema:Person
149 sg:person.013076544445.17 schema:affiliation grid-institutes:grid.493309.4
150 schema:familyName Atanassova
151 schema:givenName Vassia
152 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.013076544445.17
153 rdf:type schema:Person
154 sg:person.015745057111.08 schema:affiliation grid-institutes:grid.493309.4
155 schema:familyName Roeva
156 schema:givenName Olympia
157 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.015745057111.08
158 rdf:type schema:Person
159 grid-institutes:grid.410344.6 schema:alternateName Institute of Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria
160 schema:name Institute of Information and Communication Technology, Bulgarian Academy of Sciences, Sofia, Bulgaria
161 rdf:type schema:Organization
162 grid-institutes:grid.493309.4 schema:alternateName Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria
163 schema:name Institute of Biophysics and Biomedical Engineering, Bulgarian Academy of Sciences, Sofia, Bulgaria
164 rdf:type schema:Organization
 




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


...