Controlling Selection Area of Useful Infeasible Solutions in Directed Mating for Evolutionary Constrained Multiobjective Optimization View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2014-08-01

AUTHORS

Minami Miyakawa , Keiki Takadama , Hiroyuki Sato

ABSTRACT

As an evolutionary approach to solve multi-objective optimization problems involving several constraints, recently a MOEA using the two-stage non-dominated sorting and the directed mating (TNSDM) has been proposed. In TNSDM, the directed mating utilizes infeasible solutions dominating feasible solutions in the objective space to generate offspring. Our previous work showed that the directed mating significantly contributed to improve the search performance of TNSDM on several benchmark problems. However, the conventional directed mating has two problems. First, since the conventional directed mating selects a pair of parents based on the conventional Pareto dominance, two parents having different search directions are mated in some cases. Second, in problems with high feasibility ratio, since the number of infeasible solutions in the population is low, sometimes the directed mating cannot be performed. Consequently, the effectiveness of the directed mating cannot be obtained. To overcome these problems and further improve the effectiveness of the directed mating in TNSDM, in this work we propose a method to control selection areas of infeasible solutions by controlling dominance area of solutions (CDAS). We verify the effectiveness of the proposed method in TNSDM, and compare its search performance with the conventional CNSGA-II on m\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$m$$\end{document} objectives k\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\end{document} knapsacks problems. As results, we show that the search performance of TNSDM is further improved by controlling selection area of infeasible solutions in the directed mating. More... »

PAGES

137-152

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-319-09584-4_14

DOI

http://dx.doi.org/10.1007/978-3-319-09584-4_14

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1035017909


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": "Graduate School of Information and Engineering Sciences, The University of Electro-Communications, 1-5-1 Chofugaoka, 182-8585, Chofu, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.266298.1", 
          "name": [
            "Graduate School of Information and Engineering Sciences, The University of Electro-Communications, 1-5-1 Chofugaoka, 182-8585, Chofu, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Miyakawa", 
        "givenName": "Minami", 
        "id": "sg:person.012125104233.31", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012125104233.31"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Graduate School of Information and Engineering Sciences, The University of Electro-Communications, 1-5-1 Chofugaoka, 182-8585, Chofu, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.266298.1", 
          "name": [
            "Graduate School of Information and Engineering Sciences, The University of Electro-Communications, 1-5-1 Chofugaoka, 182-8585, Chofu, Tokyo, Japan"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Takadama", 
        "givenName": "Keiki", 
        "id": "sg:person.012774267611.99", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012774267611.99"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Graduate School of Information and Engineering Sciences, The University of Electro-Communications, 1-5-1 Chofugaoka, 182-8585, Chofu, Tokyo, Japan", 
          "id": "http://www.grid.ac/institutes/grid.266298.1", 
          "name": [
            "Graduate School of Information and Engineering Sciences, 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": "2014-08-01", 
    "datePublishedReg": "2014-08-01", 
    "description": "As an evolutionary approach to solve multi-objective optimization problems involving several constraints, recently a MOEA using the two-stage non-dominated sorting and the directed mating (TNSDM) has been proposed. In TNSDM, the directed mating utilizes infeasible solutions dominating feasible solutions in the objective space to generate offspring. Our previous work showed that the directed mating significantly contributed to improve the search performance of TNSDM on several benchmark problems. However, the conventional directed mating has two problems. First, since the conventional directed mating selects a pair of parents based on the conventional Pareto dominance, two parents having different search directions are mated in some cases. Second, in problems with high feasibility ratio, since the number of infeasible solutions in the population is low, sometimes the directed mating cannot be performed. Consequently, the effectiveness of the directed mating cannot be obtained. To overcome these problems and further improve the effectiveness of the directed mating in TNSDM, in this work we propose a method to control selection areas of infeasible solutions by controlling dominance area of solutions (CDAS). We verify the effectiveness of the proposed method in TNSDM, and compare its search performance with the conventional CNSGA-II on m\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$m$$\\end{document} objectives k\\documentclass[12pt]{minimal}\n\t\t\t\t\\usepackage{amsmath}\n\t\t\t\t\\usepackage{wasysym}\n\t\t\t\t\\usepackage{amsfonts}\n\t\t\t\t\\usepackage{amssymb}\n\t\t\t\t\\usepackage{amsbsy}\n\t\t\t\t\\usepackage{mathrsfs}\n\t\t\t\t\\usepackage{upgreek}\n\t\t\t\t\\setlength{\\oddsidemargin}{-69pt}\n\t\t\t\t\\begin{document}$$k$$\\end{document} knapsacks problems. As results, we show that the search performance of TNSDM is further improved by controlling selection area of infeasible solutions in the directed mating.", 
    "editor": [
      {
        "familyName": "Pardalos", 
        "givenName": "Panos M.", 
        "type": "Person"
      }, 
      {
        "familyName": "Resende", 
        "givenName": "Mauricio G.C.", 
        "type": "Person"
      }, 
      {
        "familyName": "Vogiatzis", 
        "givenName": "Chrysafis", 
        "type": "Person"
      }, 
      {
        "familyName": "Walteros", 
        "givenName": "Jose L.", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-319-09584-4_14", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": {
      "isbn": [
        "978-3-319-09583-7", 
        "978-3-319-09584-4"
      ], 
      "name": "Learning and Intelligent Optimization", 
      "type": "Book"
    }, 
    "keywords": [
      "infeasible solutions", 
      "multi-objective optimization problem", 
      "conventional Pareto dominance", 
      "non-dominated sorting", 
      "useful infeasible solutions", 
      "different search directions", 
      "two-stage non-dominated sorting", 
      "objective space", 
      "optimization problem", 
      "conventional CNSGA-II", 
      "search direction", 
      "Directed Mating", 
      "benchmark problems", 
      "TNSDM", 
      "Pareto dominance", 
      "dominance area", 
      "multiobjective optimization", 
      "feasibility ratio", 
      "knapsack problem", 
      "feasible solution", 
      "search performance", 
      "selection area", 
      "Evolutionary Constrained Multiobjective Optimization", 
      "problem", 
      "solution", 
      "evolutionary approach", 
      "MOEA", 
      "previous work", 
      "optimization", 
      "space", 
      "constraints", 
      "effectiveness", 
      "performance", 
      "work", 
      "approach", 
      "direction", 
      "number", 
      "pairs", 
      "pairs of parents", 
      "cases", 
      "results", 
      "selects", 
      "objective", 
      "ratio", 
      "area", 
      "sorting", 
      "dominance", 
      "population", 
      "method", 
      "mating", 
      "offspring", 
      "parents"
    ], 
    "name": "Controlling Selection Area of Useful Infeasible Solutions in Directed Mating for Evolutionary Constrained Multiobjective Optimization", 
    "pagination": "137-152", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1035017909"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-319-09584-4_14"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-319-09584-4_14", 
      "https://app.dimensions.ai/details/publication/pub.1035017909"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-05-10T10:38", 
    "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_137.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-319-09584-4_14"
  }
]
 

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.10.1007/978-3-319-09584-4_14'

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

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-09584-4_14'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-09584-4_14'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-319-09584-4_14'


 

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

141 TRIPLES      23 PREDICATES      77 URIs      70 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-319-09584-4_14 schema:about anzsrc-for:01
2 anzsrc-for:0103
3 schema:author N2bed3c98ed3e4be4a1d9eb2b2e4675bd
4 schema:datePublished 2014-08-01
5 schema:datePublishedReg 2014-08-01
6 schema:description As an evolutionary approach to solve multi-objective optimization problems involving several constraints, recently a MOEA using the two-stage non-dominated sorting and the directed mating (TNSDM) has been proposed. In TNSDM, the directed mating utilizes infeasible solutions dominating feasible solutions in the objective space to generate offspring. Our previous work showed that the directed mating significantly contributed to improve the search performance of TNSDM on several benchmark problems. However, the conventional directed mating has two problems. First, since the conventional directed mating selects a pair of parents based on the conventional Pareto dominance, two parents having different search directions are mated in some cases. Second, in problems with high feasibility ratio, since the number of infeasible solutions in the population is low, sometimes the directed mating cannot be performed. Consequently, the effectiveness of the directed mating cannot be obtained. To overcome these problems and further improve the effectiveness of the directed mating in TNSDM, in this work we propose a method to control selection areas of infeasible solutions by controlling dominance area of solutions (CDAS). We verify the effectiveness of the proposed method in TNSDM, and compare its search performance with the conventional CNSGA-II on m\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$m$$\end{document} objectives k\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$k$$\end{document} knapsacks problems. As results, we show that the search performance of TNSDM is further improved by controlling selection area of infeasible solutions in the directed mating.
7 schema:editor N7f2a5d4bd8a4451495d35c25e04f787f
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree false
11 schema:isPartOf Nd03675fe776c41af910f0399547ec709
12 schema:keywords Directed Mating
13 Evolutionary Constrained Multiobjective Optimization
14 MOEA
15 Pareto dominance
16 TNSDM
17 approach
18 area
19 benchmark problems
20 cases
21 constraints
22 conventional CNSGA-II
23 conventional Pareto dominance
24 different search directions
25 direction
26 dominance
27 dominance area
28 effectiveness
29 evolutionary approach
30 feasibility ratio
31 feasible solution
32 infeasible solutions
33 knapsack problem
34 mating
35 method
36 multi-objective optimization problem
37 multiobjective optimization
38 non-dominated sorting
39 number
40 objective
41 objective space
42 offspring
43 optimization
44 optimization problem
45 pairs
46 pairs of parents
47 parents
48 performance
49 population
50 previous work
51 problem
52 ratio
53 results
54 search direction
55 search performance
56 selection area
57 selects
58 solution
59 sorting
60 space
61 two-stage non-dominated sorting
62 useful infeasible solutions
63 work
64 schema:name Controlling Selection Area of Useful Infeasible Solutions in Directed Mating for Evolutionary Constrained Multiobjective Optimization
65 schema:pagination 137-152
66 schema:productId Nf806432ad2c948298c290173590f044b
67 Nff5c9ea08e844956b09c28c4152185fa
68 schema:publisher N26e62cf7642b43c397abf2076e1f2cfd
69 schema:sameAs https://app.dimensions.ai/details/publication/pub.1035017909
70 https://doi.org/10.1007/978-3-319-09584-4_14
71 schema:sdDatePublished 2022-05-10T10:38
72 schema:sdLicense https://scigraph.springernature.com/explorer/license/
73 schema:sdPublisher Nf23cb458b83e4dbd870bfc04013b9588
74 schema:url https://doi.org/10.1007/978-3-319-09584-4_14
75 sgo:license sg:explorer/license/
76 sgo:sdDataset chapters
77 rdf:type schema:Chapter
78 N175c846acf3e47af82eb3c01fab2845c rdf:first N7f55cae3d87f4e60b45a56a98888587c
79 rdf:rest N7a81f72643a94756a8f8eef249dbfc4e
80 N26e62cf7642b43c397abf2076e1f2cfd schema:name Springer Nature
81 rdf:type schema:Organisation
82 N2b26f81998484e098745f4b0dd3211cb schema:familyName Pardalos
83 schema:givenName Panos M.
84 rdf:type schema:Person
85 N2bed3c98ed3e4be4a1d9eb2b2e4675bd rdf:first sg:person.012125104233.31
86 rdf:rest N7317dc34fb15482b977058a476be030e
87 N42626bc43baf41848f997864f0fb9f09 rdf:first sg:person.07750750604.05
88 rdf:rest rdf:nil
89 N700621086140446ca8a80fd3ca89e474 schema:familyName Walteros
90 schema:givenName Jose L.
91 rdf:type schema:Person
92 N7317dc34fb15482b977058a476be030e rdf:first sg:person.012774267611.99
93 rdf:rest N42626bc43baf41848f997864f0fb9f09
94 N749106e3a53346ea8a6298acdc23d4c7 schema:familyName Resende
95 schema:givenName Mauricio G.C.
96 rdf:type schema:Person
97 N7a81f72643a94756a8f8eef249dbfc4e rdf:first N700621086140446ca8a80fd3ca89e474
98 rdf:rest rdf:nil
99 N7f2a5d4bd8a4451495d35c25e04f787f rdf:first N2b26f81998484e098745f4b0dd3211cb
100 rdf:rest Nbab7205c1e1d4622924a7972833cebd8
101 N7f55cae3d87f4e60b45a56a98888587c schema:familyName Vogiatzis
102 schema:givenName Chrysafis
103 rdf:type schema:Person
104 Nbab7205c1e1d4622924a7972833cebd8 rdf:first N749106e3a53346ea8a6298acdc23d4c7
105 rdf:rest N175c846acf3e47af82eb3c01fab2845c
106 Nd03675fe776c41af910f0399547ec709 schema:isbn 978-3-319-09583-7
107 978-3-319-09584-4
108 schema:name Learning and Intelligent Optimization
109 rdf:type schema:Book
110 Nf23cb458b83e4dbd870bfc04013b9588 schema:name Springer Nature - SN SciGraph project
111 rdf:type schema:Organization
112 Nf806432ad2c948298c290173590f044b schema:name dimensions_id
113 schema:value pub.1035017909
114 rdf:type schema:PropertyValue
115 Nff5c9ea08e844956b09c28c4152185fa schema:name doi
116 schema:value 10.1007/978-3-319-09584-4_14
117 rdf:type schema:PropertyValue
118 anzsrc-for:01 schema:inDefinedTermSet anzsrc-for:
119 schema:name Mathematical Sciences
120 rdf:type schema:DefinedTerm
121 anzsrc-for:0103 schema:inDefinedTermSet anzsrc-for:
122 schema:name Numerical and Computational Mathematics
123 rdf:type schema:DefinedTerm
124 sg:person.012125104233.31 schema:affiliation grid-institutes:grid.266298.1
125 schema:familyName Miyakawa
126 schema:givenName Minami
127 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012125104233.31
128 rdf:type schema:Person
129 sg:person.012774267611.99 schema:affiliation grid-institutes:grid.266298.1
130 schema:familyName Takadama
131 schema:givenName Keiki
132 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012774267611.99
133 rdf:type schema:Person
134 sg:person.07750750604.05 schema:affiliation grid-institutes:grid.266298.1
135 schema:familyName Sato
136 schema:givenName Hiroyuki
137 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07750750604.05
138 rdf:type schema:Person
139 grid-institutes:grid.266298.1 schema:alternateName Graduate School of Information and Engineering Sciences, The University of Electro-Communications, 1-5-1 Chofugaoka, 182-8585, Chofu, Tokyo, Japan
140 schema:name Graduate School of Information and Engineering Sciences, The University of Electro-Communications, 1-5-1 Chofugaoka, 182-8585, Chofu, Tokyo, Japan
141 rdf:type schema:Organization
 




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


...