Ontology type: schema:ScholarlyArticle
2015-05-14
AUTHORSMinami Miyakawa, Keiki Takadama, Hiroyuki Sato
ABSTRACTAs an evolutionary approach to solve constrained multi-objective optimization problems (CMOPs), recently an algorithm using the two-stage non-dominated sorting and the directed mating (TNSDM) was proposed. In TNSDM, the directed mating utilizes infeasible solutions dominating feasible solutions in the objective space to generate offspring. The directed mating significantly contributes to the search performance improvement in evolutionary constrained multi-objective optimization. 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 may be mated. Second, the directed mating cannot be performed in some cases especially when the population has few useful infeasible solutions. In this case, the conventional mating using only feasible solutions is performed instead. Thus, the effectiveness of the directed mating cannot always be achieved depending on the number of useful infeasible solutions. To overcome these problems and further enhance the effect of the directed mating in TNSDM, in this work we propose a method to control the selection area of useful 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 discrete m-objective k-knapsack problems and continuous mCDTLZ problems. The experimental results show that the search performance of TNSDM is further improved by controlling the selection area of useful infeasible solutions in the directed mating. More... »
PAGES25-46
http://scigraph.springernature.com/pub.10.1007/s10472-015-9455-9
DOIhttp://dx.doi.org/10.1007/s10472-015-9455-9
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1032103303
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, Chofu, 182-8585, 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, Chofu, 182-8585, 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, Chofu, 182-8585, 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, Chofu, 182-8585, 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, Chofu, 182-8585, 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, Chofu, 182-8585, 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"
}
],
"citation": [
{
"id": "sg:pub.10.1007/978-3-540-70928-2_5",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1038012782",
"https://doi.org/10.1007/978-3-540-70928-2_5"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-540-72964-8_3",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1048620556",
"https://doi.org/10.1007/978-3-540-72964-8_3"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-642-00619-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040808462",
"https://doi.org/10.1007/978-3-642-00619-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-0-387-77247-9",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1028068980",
"https://doi.org/10.1007/978-0-387-77247-9"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-0-85729-652-8_1",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1040277102",
"https://doi.org/10.1007/978-0-85729-652-8_1"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-540-24777-7",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1020901084",
"https://doi.org/10.1007/978-3-540-24777-7"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/978-3-319-09584-4_14",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1035017909",
"https://doi.org/10.1007/978-3-319-09584-4_14"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10472-012-9293-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1000508369",
"https://doi.org/10.1007/s10472-012-9293-y"
],
"type": "CreativeWork"
}
],
"datePublished": "2015-05-14",
"datePublishedReg": "2015-05-14",
"description": "As an evolutionary approach to solve constrained multi-objective optimization problems (CMOPs), recently an algorithm using the two-stage non-dominated sorting and the directed mating (TNSDM) was proposed. In TNSDM, the directed mating utilizes infeasible solutions dominating feasible solutions in the objective space to generate offspring. The directed mating significantly contributes to the search performance improvement in evolutionary constrained multi-objective optimization. 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 may be mated. Second, the directed mating cannot be performed in some cases especially when the population has few useful infeasible solutions. In this case, the conventional mating using only feasible solutions is performed instead. Thus, the effectiveness of the directed mating cannot always be achieved depending on the number of useful infeasible solutions. To overcome these problems and further enhance the effect of the directed mating in TNSDM, in this work we propose a method to control the selection area of useful 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 discrete m-objective k-knapsack problems and continuous mCDTLZ problems. The experimental results show that the search performance of TNSDM is further improved by controlling the selection area of useful infeasible solutions in the directed mating.",
"genre": "article",
"id": "sg:pub.10.1007/s10472-015-9455-9",
"inLanguage": "en",
"isAccessibleForFree": false,
"isFundedItemOf": [
{
"id": "sg:grant.5831069",
"type": "MonetaryGrant"
}
],
"isPartOf": [
{
"id": "sg:journal.1043955",
"issn": [
"1012-2443",
"1573-7470"
],
"name": "Annals of Mathematics and Artificial Intelligence",
"publisher": "Springer Nature",
"type": "Periodical"
},
{
"issueNumber": "1-2",
"type": "PublicationIssue"
},
{
"type": "PublicationVolume",
"volumeNumber": "76"
}
],
"keywords": [
"useful infeasible solutions",
"multi-objective optimization",
"infeasible solutions",
"constrained multi-objective optimization",
"multi-objective optimization problem",
"conventional Pareto dominance",
"non-dominated sorting",
"different search directions",
"feasible solution",
"two-stage non-dominated sorting",
"objective space",
"optimization problem",
"conventional CNSGA-II",
"search direction",
"Pareto dominance",
"TNSDM",
"dominance area",
"knapsack problem",
"search performance",
"selection area",
"optimization",
"problem",
"solution",
"only feasible solution",
"evolutionary approach",
"experimental results",
"conventional mating",
"performance improvement",
"algorithm",
"space",
"effectiveness",
"performance",
"search performance improvement",
"cases",
"approach",
"direction",
"number",
"pairs",
"pairs of parents",
"work",
"results",
"selects",
"area",
"effect",
"improvement",
"sorting",
"dominance",
"method",
"population",
"mating",
"offspring",
"parents"
],
"name": "Controlling selection areas of useful infeasible solutions for directed mating in evolutionary constrained multi-objective optimization",
"pagination": "25-46",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1032103303"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s10472-015-9455-9"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s10472-015-9455-9",
"https://app.dimensions.ai/details/publication/pub.1032103303"
],
"sdDataset": "articles",
"sdDatePublished": "2022-05-10T10:13",
"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/article/article_682.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s10472-015-9455-9"
}
]
Download the RDF metadata as: json-ld nt turtle xml License info
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/s10472-015-9455-9'
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/s10472-015-9455-9'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10472-015-9455-9'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10472-015-9455-9'
This table displays all metadata directly associated to this object as RDF triples.
158 TRIPLES
22 PREDICATES
85 URIs
69 LITERALS
6 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1007/s10472-015-9455-9 | schema:about | anzsrc-for:01 |
2 | ″ | ″ | anzsrc-for:0103 |
3 | ″ | schema:author | N881a1371307b429bacc91660152bcf62 |
4 | ″ | schema:citation | sg:pub.10.1007/978-0-387-77247-9 |
5 | ″ | ″ | sg:pub.10.1007/978-0-85729-652-8_1 |
6 | ″ | ″ | sg:pub.10.1007/978-3-319-09584-4_14 |
7 | ″ | ″ | sg:pub.10.1007/978-3-540-24777-7 |
8 | ″ | ″ | sg:pub.10.1007/978-3-540-70928-2_5 |
9 | ″ | ″ | sg:pub.10.1007/978-3-540-72964-8_3 |
10 | ″ | ″ | sg:pub.10.1007/978-3-642-00619-7 |
11 | ″ | ″ | sg:pub.10.1007/s10472-012-9293-y |
12 | ″ | schema:datePublished | 2015-05-14 |
13 | ″ | schema:datePublishedReg | 2015-05-14 |
14 | ″ | schema:description | As an evolutionary approach to solve constrained multi-objective optimization problems (CMOPs), recently an algorithm using the two-stage non-dominated sorting and the directed mating (TNSDM) was proposed. In TNSDM, the directed mating utilizes infeasible solutions dominating feasible solutions in the objective space to generate offspring. The directed mating significantly contributes to the search performance improvement in evolutionary constrained multi-objective optimization. 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 may be mated. Second, the directed mating cannot be performed in some cases especially when the population has few useful infeasible solutions. In this case, the conventional mating using only feasible solutions is performed instead. Thus, the effectiveness of the directed mating cannot always be achieved depending on the number of useful infeasible solutions. To overcome these problems and further enhance the effect of the directed mating in TNSDM, in this work we propose a method to control the selection area of useful 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 discrete m-objective k-knapsack problems and continuous mCDTLZ problems. The experimental results show that the search performance of TNSDM is further improved by controlling the selection area of useful infeasible solutions in the directed mating. |
15 | ″ | schema:genre | article |
16 | ″ | schema:inLanguage | en |
17 | ″ | schema:isAccessibleForFree | false |
18 | ″ | schema:isPartOf | N01e81c9b36114786a1251a627260b66d |
19 | ″ | ″ | N7dc83618d8b749a6aefb297b412a89a8 |
20 | ″ | ″ | sg:journal.1043955 |
21 | ″ | schema:keywords | Pareto dominance |
22 | ″ | ″ | TNSDM |
23 | ″ | ″ | algorithm |
24 | ″ | ″ | approach |
25 | ″ | ″ | area |
26 | ″ | ″ | cases |
27 | ″ | ″ | constrained multi-objective optimization |
28 | ″ | ″ | conventional CNSGA-II |
29 | ″ | ″ | conventional Pareto dominance |
30 | ″ | ″ | conventional mating |
31 | ″ | ″ | different search directions |
32 | ″ | ″ | direction |
33 | ″ | ″ | dominance |
34 | ″ | ″ | dominance area |
35 | ″ | ″ | effect |
36 | ″ | ″ | effectiveness |
37 | ″ | ″ | evolutionary approach |
38 | ″ | ″ | experimental results |
39 | ″ | ″ | feasible solution |
40 | ″ | ″ | improvement |
41 | ″ | ″ | infeasible solutions |
42 | ″ | ″ | knapsack problem |
43 | ″ | ″ | mating |
44 | ″ | ″ | method |
45 | ″ | ″ | multi-objective optimization |
46 | ″ | ″ | multi-objective optimization problem |
47 | ″ | ″ | non-dominated sorting |
48 | ″ | ″ | number |
49 | ″ | ″ | objective space |
50 | ″ | ″ | offspring |
51 | ″ | ″ | only feasible solution |
52 | ″ | ″ | optimization |
53 | ″ | ″ | optimization problem |
54 | ″ | ″ | pairs |
55 | ″ | ″ | pairs of parents |
56 | ″ | ″ | parents |
57 | ″ | ″ | performance |
58 | ″ | ″ | performance improvement |
59 | ″ | ″ | population |
60 | ″ | ″ | problem |
61 | ″ | ″ | results |
62 | ″ | ″ | search direction |
63 | ″ | ″ | search performance |
64 | ″ | ″ | search performance improvement |
65 | ″ | ″ | selection area |
66 | ″ | ″ | selects |
67 | ″ | ″ | solution |
68 | ″ | ″ | sorting |
69 | ″ | ″ | space |
70 | ″ | ″ | two-stage non-dominated sorting |
71 | ″ | ″ | useful infeasible solutions |
72 | ″ | ″ | work |
73 | ″ | schema:name | Controlling selection areas of useful infeasible solutions for directed mating in evolutionary constrained multi-objective optimization |
74 | ″ | schema:pagination | 25-46 |
75 | ″ | schema:productId | N6485fa2623fa467ba1719c8ecdac2498 |
76 | ″ | ″ | Nafefb2833f694b4fba2f484015179d24 |
77 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1032103303 |
78 | ″ | ″ | https://doi.org/10.1007/s10472-015-9455-9 |
79 | ″ | schema:sdDatePublished | 2022-05-10T10:13 |
80 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
81 | ″ | schema:sdPublisher | N299434399868499aaed71813128435b6 |
82 | ″ | schema:url | https://doi.org/10.1007/s10472-015-9455-9 |
83 | ″ | sgo:license | sg:explorer/license/ |
84 | ″ | sgo:sdDataset | articles |
85 | ″ | rdf:type | schema:ScholarlyArticle |
86 | N01e81c9b36114786a1251a627260b66d | schema:issueNumber | 1-2 |
87 | ″ | rdf:type | schema:PublicationIssue |
88 | N299434399868499aaed71813128435b6 | schema:name | Springer Nature - SN SciGraph project |
89 | ″ | rdf:type | schema:Organization |
90 | N6485fa2623fa467ba1719c8ecdac2498 | schema:name | doi |
91 | ″ | schema:value | 10.1007/s10472-015-9455-9 |
92 | ″ | rdf:type | schema:PropertyValue |
93 | N6f78e75fdcd74bb2a9200d84ec0c9000 | rdf:first | sg:person.012774267611.99 |
94 | ″ | rdf:rest | Na8b650fc9b634e56b3f372899937a71c |
95 | N7dc83618d8b749a6aefb297b412a89a8 | schema:volumeNumber | 76 |
96 | ″ | rdf:type | schema:PublicationVolume |
97 | N881a1371307b429bacc91660152bcf62 | rdf:first | sg:person.012125104233.31 |
98 | ″ | rdf:rest | N6f78e75fdcd74bb2a9200d84ec0c9000 |
99 | Na8b650fc9b634e56b3f372899937a71c | rdf:first | sg:person.07750750604.05 |
100 | ″ | rdf:rest | rdf:nil |
101 | Nafefb2833f694b4fba2f484015179d24 | schema:name | dimensions_id |
102 | ″ | schema:value | pub.1032103303 |
103 | ″ | rdf:type | schema:PropertyValue |
104 | anzsrc-for:01 | schema:inDefinedTermSet | anzsrc-for: |
105 | ″ | schema:name | Mathematical Sciences |
106 | ″ | rdf:type | schema:DefinedTerm |
107 | anzsrc-for:0103 | schema:inDefinedTermSet | anzsrc-for: |
108 | ″ | schema:name | Numerical and Computational Mathematics |
109 | ″ | rdf:type | schema:DefinedTerm |
110 | sg:grant.5831069 | http://pending.schema.org/fundedItem | sg:pub.10.1007/s10472-015-9455-9 |
111 | ″ | rdf:type | schema:MonetaryGrant |
112 | sg:journal.1043955 | schema:issn | 1012-2443 |
113 | ″ | ″ | 1573-7470 |
114 | ″ | schema:name | Annals of Mathematics and Artificial Intelligence |
115 | ″ | schema:publisher | Springer Nature |
116 | ″ | rdf:type | schema:Periodical |
117 | sg:person.012125104233.31 | schema:affiliation | grid-institutes:grid.266298.1 |
118 | ″ | schema:familyName | Miyakawa |
119 | ″ | schema:givenName | Minami |
120 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012125104233.31 |
121 | ″ | rdf:type | schema:Person |
122 | sg:person.012774267611.99 | schema:affiliation | grid-institutes:grid.266298.1 |
123 | ″ | schema:familyName | Takadama |
124 | ″ | schema:givenName | Keiki |
125 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012774267611.99 |
126 | ″ | rdf:type | schema:Person |
127 | sg:person.07750750604.05 | schema:affiliation | grid-institutes:grid.266298.1 |
128 | ″ | schema:familyName | Sato |
129 | ″ | schema:givenName | Hiroyuki |
130 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.07750750604.05 |
131 | ″ | rdf:type | schema:Person |
132 | sg:pub.10.1007/978-0-387-77247-9 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1028068980 |
133 | ″ | ″ | https://doi.org/10.1007/978-0-387-77247-9 |
134 | ″ | rdf:type | schema:CreativeWork |
135 | sg:pub.10.1007/978-0-85729-652-8_1 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1040277102 |
136 | ″ | ″ | https://doi.org/10.1007/978-0-85729-652-8_1 |
137 | ″ | rdf:type | schema:CreativeWork |
138 | sg:pub.10.1007/978-3-319-09584-4_14 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1035017909 |
139 | ″ | ″ | https://doi.org/10.1007/978-3-319-09584-4_14 |
140 | ″ | rdf:type | schema:CreativeWork |
141 | sg:pub.10.1007/978-3-540-24777-7 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1020901084 |
142 | ″ | ″ | https://doi.org/10.1007/978-3-540-24777-7 |
143 | ″ | rdf:type | schema:CreativeWork |
144 | sg:pub.10.1007/978-3-540-70928-2_5 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1038012782 |
145 | ″ | ″ | https://doi.org/10.1007/978-3-540-70928-2_5 |
146 | ″ | rdf:type | schema:CreativeWork |
147 | sg:pub.10.1007/978-3-540-72964-8_3 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1048620556 |
148 | ″ | ″ | https://doi.org/10.1007/978-3-540-72964-8_3 |
149 | ″ | rdf:type | schema:CreativeWork |
150 | sg:pub.10.1007/978-3-642-00619-7 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1040808462 |
151 | ″ | ″ | https://doi.org/10.1007/978-3-642-00619-7 |
152 | ″ | rdf:type | schema:CreativeWork |
153 | sg:pub.10.1007/s10472-012-9293-y | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1000508369 |
154 | ″ | ″ | https://doi.org/10.1007/s10472-012-9293-y |
155 | ″ | rdf:type | schema:CreativeWork |
156 | grid-institutes:grid.266298.1 | schema:alternateName | Graduate School of Information and Engineering Sciences, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, 182-8585, Tokyo, Japan |
157 | ″ | schema:name | Graduate School of Information and Engineering Sciences, The University of Electro-Communications, 1-5-1 Chofugaoka, Chofu, 182-8585, Tokyo, Japan |
158 | ″ | rdf:type | schema:Organization |