Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2007-01-01

AUTHORS

Tobias Wagner , Nicola Beume , Boris Naujoks

ABSTRACT

Research within the area of Evolutionary Multi-objective Optimization (EMO) focused on two- and three-dimensional objective functions, so far. Most algorithms have been developed for and tested on this limited application area. To broaden the insight in the behavior of EMO algorithms (EMOA) in higher dimensional objective spaces, a comprehensive benchmarking is presented, featuring several state-of-the-art EMOA, as well as an aggregative approach and a restart strategy on established scalable test problems with three to six objectives. It is demonstrated why the performance of well-established EMOA (NSGA-II, SPEA2) rapidly degradates with increasing dimension. Newer EMOA like ε-MOEA, MSOPS, IBEA and SMS-EMOA cope very well with high-dimensional objective spaces. Their specific advantages and drawbacks are illustrated, thus giving valuable hints for practitioners which EMOA to choose depending on the optimization scenario. Additionally, a new method for the generation of weight vectors usable in aggregation methods is presented. More... »

PAGES

742-756

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-540-70928-2_56

DOI

http://dx.doi.org/10.1007/978-3-540-70928-2_56

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1040936709


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/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing 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"
      }, 
      {
        "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"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Institut f\u00fcr Spanende Fertigung (ISF)", 
          "id": "http://www.grid.ac/institutes/None", 
          "name": [
            "Institut f\u00fcr Spanende Fertigung (ISF)"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Wagner", 
        "givenName": "Tobias", 
        "id": "sg:person.01235065761.06", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01235065761.06"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Chair of Algorithm Engineering, University of Dortmund, 44221 Dortmund, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5675.1", 
          "name": [
            "Chair of Algorithm Engineering, University of Dortmund, 44221 Dortmund, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Beume", 
        "givenName": "Nicola", 
        "id": "sg:person.016541700456.01", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016541700456.01"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Chair of Algorithm Engineering, University of Dortmund, 44221 Dortmund, Germany", 
          "id": "http://www.grid.ac/institutes/grid.5675.1", 
          "name": [
            "Chair of Algorithm Engineering, University of Dortmund, 44221 Dortmund, Germany"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Naujoks", 
        "givenName": "Boris", 
        "id": "sg:person.012206275603.21", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012206275603.21"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2007-01-01", 
    "datePublishedReg": "2007-01-01", 
    "description": "Research within the area of Evolutionary Multi-objective Optimization (EMO) focused on two- and three-dimensional objective functions, so far. Most algorithms have been developed for and tested on this limited application area. To broaden the insight in the behavior of EMO algorithms (EMOA) in higher dimensional objective spaces, a comprehensive benchmarking is presented, featuring several state-of-the-art EMOA, as well as an aggregative approach and a restart strategy on established scalable test problems with three to six objectives. It is demonstrated why the performance of well-established EMOA (NSGA-II, SPEA2) rapidly degradates with increasing dimension. Newer EMOA like \u03b5-MOEA, MSOPS, IBEA and SMS-EMOA cope very well with high-dimensional objective spaces. Their specific advantages and drawbacks are illustrated, thus giving valuable hints for practitioners which EMOA to choose depending on the optimization scenario. Additionally, a new method for the generation of weight vectors usable in aggregation methods is presented.", 
    "editor": [
      {
        "familyName": "Obayashi", 
        "givenName": "Shigeru", 
        "type": "Person"
      }, 
      {
        "familyName": "Deb", 
        "givenName": "Kalyanmoy", 
        "type": "Person"
      }, 
      {
        "familyName": "Poloni", 
        "givenName": "Carlo", 
        "type": "Person"
      }, 
      {
        "familyName": "Hiroyasu", 
        "givenName": "Tomoyuki", 
        "type": "Person"
      }, 
      {
        "familyName": "Murata", 
        "givenName": "Tadahiko", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-540-70928-2_56", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-540-70927-5", 
        "978-3-540-70928-2"
      ], 
      "name": "Evolutionary Multi-Criterion Optimization", 
      "type": "Book"
    }, 
    "keywords": [
      "evolutionary multi-objective optimization", 
      "EMO algorithms", 
      "objective space", 
      "high-dimensional objective space", 
      "high dimensional objective space", 
      "multi-objective optimization", 
      "dimensional objective space", 
      "scalable test problems", 
      "art EMO algorithms", 
      "SMS-EMOA", 
      "limited application area", 
      "new EMO algorithm", 
      "objective optimization", 
      "test problems", 
      "objective function", 
      "optimization scenarios", 
      "restart strategy", 
      "aggregative approach", 
      "weight vector", 
      "comprehensive benchmarking", 
      "most algorithms", 
      "aggregation method", 
      "application areas", 
      "optimization", 
      "algorithm", 
      "new method", 
      "space", 
      "MSOPS", 
      "MOEA", 
      "Pareto", 
      "ibeA", 
      "valuable hints", 
      "problem", 
      "vector", 
      "approach", 
      "dimensions", 
      "scenarios", 
      "drawbacks", 
      "function", 
      "performance", 
      "method", 
      "benchmarking", 
      "specific advantages", 
      "advantages", 
      "objective", 
      "hints", 
      "behavior", 
      "state", 
      "strategies", 
      "generation", 
      "research", 
      "area", 
      "aggregation", 
      "practitioners", 
      "insights", 
      "indicators", 
      "three-dimensional objective functions"
    ], 
    "name": "Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization", 
    "pagination": "742-756", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1040936709"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-540-70928-2_56"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-540-70928-2_56", 
      "https://app.dimensions.ai/details/publication/pub.1040936709"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2021-11-01T18:47", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20211101/entities/gbq_results/chapter/chapter_145.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-540-70928-2_56"
  }
]
 

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.10.1007/978-3-540-70928-2_56'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-70928-2_56'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-70928-2_56'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-540-70928-2_56'


 

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

162 TRIPLES      23 PREDICATES      84 URIs      75 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-540-70928-2_56 schema:about anzsrc-for:01
2 anzsrc-for:0103
3 anzsrc-for:08
4 anzsrc-for:0801
5 schema:author Nbcafaf7b525449cebe3f28b82ab629ea
6 schema:datePublished 2007-01-01
7 schema:datePublishedReg 2007-01-01
8 schema:description Research within the area of Evolutionary Multi-objective Optimization (EMO) focused on two- and three-dimensional objective functions, so far. Most algorithms have been developed for and tested on this limited application area. To broaden the insight in the behavior of EMO algorithms (EMOA) in higher dimensional objective spaces, a comprehensive benchmarking is presented, featuring several state-of-the-art EMOA, as well as an aggregative approach and a restart strategy on established scalable test problems with three to six objectives. It is demonstrated why the performance of well-established EMOA (NSGA-II, SPEA2) rapidly degradates with increasing dimension. Newer EMOA like ε-MOEA, MSOPS, IBEA and SMS-EMOA cope very well with high-dimensional objective spaces. Their specific advantages and drawbacks are illustrated, thus giving valuable hints for practitioners which EMOA to choose depending on the optimization scenario. Additionally, a new method for the generation of weight vectors usable in aggregation methods is presented.
9 schema:editor N0e8eb90115e04ebd83eb27b363dd27a5
10 schema:genre chapter
11 schema:inLanguage en
12 schema:isAccessibleForFree false
13 schema:isPartOf N201a9803a96945879d1f3fc7f81b226d
14 schema:keywords EMO algorithms
15 MOEA
16 MSOPS
17 Pareto
18 SMS-EMOA
19 advantages
20 aggregation
21 aggregation method
22 aggregative approach
23 algorithm
24 application areas
25 approach
26 area
27 art EMO algorithms
28 behavior
29 benchmarking
30 comprehensive benchmarking
31 dimensional objective space
32 dimensions
33 drawbacks
34 evolutionary multi-objective optimization
35 function
36 generation
37 high dimensional objective space
38 high-dimensional objective space
39 hints
40 ibeA
41 indicators
42 insights
43 limited application area
44 method
45 most algorithms
46 multi-objective optimization
47 new EMO algorithm
48 new method
49 objective
50 objective function
51 objective optimization
52 objective space
53 optimization
54 optimization scenarios
55 performance
56 practitioners
57 problem
58 research
59 restart strategy
60 scalable test problems
61 scenarios
62 space
63 specific advantages
64 state
65 strategies
66 test problems
67 three-dimensional objective functions
68 valuable hints
69 vector
70 weight vector
71 schema:name Pareto-, Aggregation-, and Indicator-Based Methods in Many-Objective Optimization
72 schema:pagination 742-756
73 schema:productId Na74328be8db240849fc2a463af6fe4e0
74 Neaf8cc6636894303813364890b2797ef
75 schema:publisher N87aa161f29f046c6bdbc68abf7f3599a
76 schema:sameAs https://app.dimensions.ai/details/publication/pub.1040936709
77 https://doi.org/10.1007/978-3-540-70928-2_56
78 schema:sdDatePublished 2021-11-01T18:47
79 schema:sdLicense https://scigraph.springernature.com/explorer/license/
80 schema:sdPublisher Ne42273e198ea43818c60702cbeb093d8
81 schema:url https://doi.org/10.1007/978-3-540-70928-2_56
82 sgo:license sg:explorer/license/
83 sgo:sdDataset chapters
84 rdf:type schema:Chapter
85 N0745f301f9164d71a26b99c960069c92 rdf:first sg:person.012206275603.21
86 rdf:rest rdf:nil
87 N0987a35ce823438796c5059eaa06e89c rdf:first N73a479cf24764c88bd39384158f31c1f
88 rdf:rest Nc1e848c6bec04312b19d9328b64be5c7
89 N099062cd36d847e5a22aaeebdf998487 schema:familyName Obayashi
90 schema:givenName Shigeru
91 rdf:type schema:Person
92 N0e8eb90115e04ebd83eb27b363dd27a5 rdf:first N099062cd36d847e5a22aaeebdf998487
93 rdf:rest N1c8af0b88aba416f8a1dada7d6c95659
94 N1c8af0b88aba416f8a1dada7d6c95659 rdf:first Nce84683803414100b1b7baf04cdc9138
95 rdf:rest N0987a35ce823438796c5059eaa06e89c
96 N201a9803a96945879d1f3fc7f81b226d schema:isbn 978-3-540-70927-5
97 978-3-540-70928-2
98 schema:name Evolutionary Multi-Criterion Optimization
99 rdf:type schema:Book
100 N4006e7c67ed8466fb9d2e6253fdd135c rdf:first N668da8e3d7f04d88a4e7b4208430fe1b
101 rdf:rest rdf:nil
102 N668da8e3d7f04d88a4e7b4208430fe1b schema:familyName Murata
103 schema:givenName Tadahiko
104 rdf:type schema:Person
105 N6f96524e0fa5454e8dfd2b287f364012 schema:familyName Hiroyasu
106 schema:givenName Tomoyuki
107 rdf:type schema:Person
108 N73a479cf24764c88bd39384158f31c1f schema:familyName Poloni
109 schema:givenName Carlo
110 rdf:type schema:Person
111 N87aa161f29f046c6bdbc68abf7f3599a schema:name Springer Nature
112 rdf:type schema:Organisation
113 N8c3558bbbd6c41c49c8e320a5a26830f rdf:first sg:person.016541700456.01
114 rdf:rest N0745f301f9164d71a26b99c960069c92
115 Na74328be8db240849fc2a463af6fe4e0 schema:name doi
116 schema:value 10.1007/978-3-540-70928-2_56
117 rdf:type schema:PropertyValue
118 Nbcafaf7b525449cebe3f28b82ab629ea rdf:first sg:person.01235065761.06
119 rdf:rest N8c3558bbbd6c41c49c8e320a5a26830f
120 Nc1e848c6bec04312b19d9328b64be5c7 rdf:first N6f96524e0fa5454e8dfd2b287f364012
121 rdf:rest N4006e7c67ed8466fb9d2e6253fdd135c
122 Nce84683803414100b1b7baf04cdc9138 schema:familyName Deb
123 schema:givenName Kalyanmoy
124 rdf:type schema:Person
125 Ne42273e198ea43818c60702cbeb093d8 schema:name Springer Nature - SN SciGraph project
126 rdf:type schema:Organization
127 Neaf8cc6636894303813364890b2797ef schema:name dimensions_id
128 schema:value pub.1040936709
129 rdf:type schema:PropertyValue
130 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
131 schema:name Mathematical Sciences
132 rdf:type schema:DefinedTerm
133 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
134 schema:name Numerical and Computational Mathematics
135 rdf:type schema:DefinedTerm
136 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
137 schema:name Information and Computing Sciences
138 rdf:type schema:DefinedTerm
139 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
140 schema:name Artificial Intelligence and Image Processing
141 rdf:type schema:DefinedTerm
142 sg:person.012206275603.21 schema:affiliation grid-institutes:grid.5675.1
143 schema:familyName Naujoks
144 schema:givenName Boris
145 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012206275603.21
146 rdf:type schema:Person
147 sg:person.01235065761.06 schema:affiliation grid-institutes:None
148 schema:familyName Wagner
149 schema:givenName Tobias
150 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01235065761.06
151 rdf:type schema:Person
152 sg:person.016541700456.01 schema:affiliation grid-institutes:grid.5675.1
153 schema:familyName Beume
154 schema:givenName Nicola
155 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016541700456.01
156 rdf:type schema:Person
157 grid-institutes:None schema:alternateName Institut für Spanende Fertigung (ISF)
158 schema:name Institut für Spanende Fertigung (ISF)
159 rdf:type schema:Organization
160 grid-institutes:grid.5675.1 schema:alternateName Chair of Algorithm Engineering, University of Dortmund, 44221 Dortmund, Germany
161 schema:name Chair of Algorithm Engineering, University of Dortmund, 44221 Dortmund, Germany
162 rdf:type schema:Organization
 




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


...