Ontology type: schema:ScholarlyArticle
2021-11-03
AUTHORSLianguo Wang, Xiaojuan Liu
ABSTRACTThe shuffled frog leaping algorithm is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize the complex functions problems. To overcome the above shortcomings, a shuffled frog leaping algorithm with contraction factor was proposed. By introducing acceleration factors c1 and c2, the ability of worst individual to learn from best individual within the submemeplexes or global best individual of the entire population was improved and the convergence rate of algorithm was accelerated. Under inserting the contraction factor χ, the convergence of algorithm was ensured. After performing local optimization of the self-learning operator on the worst individual, and taking full advantage of the useful information in the worst individuals, the self-learning ability of the individual and the optimization accuracy of the algorithm were improved. Simulation results illustrated that the enhanced algorithm performed better optimization performance than basic SFLA and other improved SFLAs. Finally, the proposed algorithm was used to optimize five problems of the mechanical design, and its validity and practicability were verified. More... »
PAGES1-19
http://scigraph.springernature.com/pub.10.1007/s00366-021-01510-8
DOIhttp://dx.doi.org/10.1007/s00366-021-01510-8
DIMENSIONShttps://app.dimensions.ai/details/publication/pub.1142376306
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": "College of Information Science and Technology, Gansu Agricultural University, No. 1 Yingmen Village, 730070, Lanzhou, Gansu, China",
"id": "http://www.grid.ac/institutes/grid.411734.4",
"name": [
"College of Information Science and Technology, Gansu Agricultural University, No. 1 Yingmen Village, 730070, Lanzhou, Gansu, China"
],
"type": "Organization"
},
"familyName": "Wang",
"givenName": "Lianguo",
"id": "sg:person.011546457215.07",
"sameAs": [
"https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011546457215.07"
],
"type": "Person"
},
{
"affiliation": {
"alternateName": "College of Information Science and Technology, Gansu Agricultural University, No. 1 Yingmen Village, 730070, Lanzhou, Gansu, China",
"id": "http://www.grid.ac/institutes/grid.411734.4",
"name": [
"College of Information Science and Technology, Gansu Agricultural University, No. 1 Yingmen Village, 730070, Lanzhou, Gansu, China"
],
"type": "Organization"
},
"familyName": "Liu",
"givenName": "Xiaojuan",
"type": "Person"
}
],
"citation": [
{
"id": "sg:pub.10.1007/978-3-662-03423-1_27",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1011772001",
"https://doi.org/10.1007/978-3-662-03423-1_27"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s11831-019-09343-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1115676632",
"https://doi.org/10.1007/s11831-019-09343-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00366-011-0241-y",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1009081493",
"https://doi.org/10.1007/s00366-011-0241-y"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s10898-007-9149-x",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1049543869",
"https://doi.org/10.1007/s10898-007-9149-x"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00500-019-04484-4",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1122284946",
"https://doi.org/10.1007/s00500-019-04484-4"
],
"type": "CreativeWork"
},
{
"id": "sg:pub.10.1007/s00170-016-9523-2",
"sameAs": [
"https://app.dimensions.ai/details/publication/pub.1016842758",
"https://doi.org/10.1007/s00170-016-9523-2"
],
"type": "CreativeWork"
}
],
"datePublished": "2021-11-03",
"datePublishedReg": "2021-11-03",
"description": "The shuffled frog leaping algorithm is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize the complex functions problems. To overcome the above shortcomings, a shuffled frog leaping algorithm with contraction factor was proposed. By introducing acceleration factors c1 and c2, the ability of worst individual to learn from best individual within the submemeplexes or global best individual of the entire population was improved and the convergence rate of algorithm was accelerated. Under inserting the contraction factor \u03c7, the convergence of algorithm was ensured. After performing local optimization of the self-learning operator on the worst individual, and taking full advantage of the useful information in the worst individuals, the self-learning ability of the individual and the optimization accuracy of the algorithm were improved. Simulation results illustrated that the enhanced algorithm performed better optimization performance than basic SFLA and other improved SFLAs. Finally, the proposed algorithm was used to optimize five problems of the mechanical design, and its validity and practicability were verified.",
"genre": "article",
"id": "sg:pub.10.1007/s00366-021-01510-8",
"inLanguage": "en",
"isAccessibleForFree": false,
"isPartOf": [
{
"id": "sg:journal.1041785",
"issn": [
"0177-0667",
"1435-5663"
],
"name": "Engineering with Computers",
"publisher": "Springer Nature",
"type": "Periodical"
}
],
"keywords": [
"optimization accuracy",
"worst individuals",
"contraction factor",
"better optimization performance",
"low optimization accuracy",
"convergence of algorithm",
"best individual",
"global best individual",
"convergence rate",
"optimization performance",
"local optimization",
"local optimum",
"function problems",
"simulation results",
"algorithm",
"optimum design",
"basic SFLA",
"SFLA",
"self-learning operator",
"problem",
"self-learning ability",
"convergence",
"factor \u03c7",
"operators",
"accuracy",
"optimization",
"full advantage",
"above shortcomings",
"mechanical design",
"optimum",
"design",
"validity",
"applications",
"practicability",
"useful information",
"performance",
"advantages",
"shortcomings",
"results",
"information",
"entire population",
"C2",
"ability",
"C1",
"rate",
"factors",
"population",
"individuals",
"factor C1",
"frogs",
"improved SFLA"
],
"name": "A shuffled frog leaping algorithm with contraction factor and its application in mechanical optimum design",
"pagination": "1-19",
"productId": [
{
"name": "dimensions_id",
"type": "PropertyValue",
"value": [
"pub.1142376306"
]
},
{
"name": "doi",
"type": "PropertyValue",
"value": [
"10.1007/s00366-021-01510-8"
]
}
],
"sameAs": [
"https://doi.org/10.1007/s00366-021-01510-8",
"https://app.dimensions.ai/details/publication/pub.1142376306"
],
"sdDataset": "articles",
"sdDatePublished": "2022-05-20T07:38",
"sdLicense": "https://scigraph.springernature.com/explorer/license/",
"sdPublisher": {
"name": "Springer Nature - SN SciGraph project",
"type": "Organization"
},
"sdSource": "s3://com-springernature-scigraph/baseset/20220519/entities/gbq_results/article/article_884.jsonl",
"type": "ScholarlyArticle",
"url": "https://doi.org/10.1007/s00366-021-01510-8"
}
]
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/s00366-021-01510-8'
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/s00366-021-01510-8'
Turtle is a human-readable linked data format.
curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s00366-021-01510-8'
RDF/XML is a standard XML format for linked data.
curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s00366-021-01510-8'
This table displays all metadata directly associated to this object as RDF triples.
133 TRIPLES
22 PREDICATES
80 URIs
66 LITERALS
4 BLANK NODES
Subject | Predicate | Object | |
---|---|---|---|
1 | sg:pub.10.1007/s00366-021-01510-8 | schema:about | anzsrc-for:01 |
2 | ″ | ″ | anzsrc-for:0103 |
3 | ″ | schema:author | Ne80b7d3c551440fb9acf3d657624da42 |
4 | ″ | schema:citation | sg:pub.10.1007/978-3-662-03423-1_27 |
5 | ″ | ″ | sg:pub.10.1007/s00170-016-9523-2 |
6 | ″ | ″ | sg:pub.10.1007/s00366-011-0241-y |
7 | ″ | ″ | sg:pub.10.1007/s00500-019-04484-4 |
8 | ″ | ″ | sg:pub.10.1007/s10898-007-9149-x |
9 | ″ | ″ | sg:pub.10.1007/s11831-019-09343-x |
10 | ″ | schema:datePublished | 2021-11-03 |
11 | ″ | schema:datePublishedReg | 2021-11-03 |
12 | ″ | schema:description | The shuffled frog leaping algorithm is easily trapped into local optimum and has the low optimization accuracy when it is used to optimize the complex functions problems. To overcome the above shortcomings, a shuffled frog leaping algorithm with contraction factor was proposed. By introducing acceleration factors c1 and c2, the ability of worst individual to learn from best individual within the submemeplexes or global best individual of the entire population was improved and the convergence rate of algorithm was accelerated. Under inserting the contraction factor χ, the convergence of algorithm was ensured. After performing local optimization of the self-learning operator on the worst individual, and taking full advantage of the useful information in the worst individuals, the self-learning ability of the individual and the optimization accuracy of the algorithm were improved. Simulation results illustrated that the enhanced algorithm performed better optimization performance than basic SFLA and other improved SFLAs. Finally, the proposed algorithm was used to optimize five problems of the mechanical design, and its validity and practicability were verified. |
13 | ″ | schema:genre | article |
14 | ″ | schema:inLanguage | en |
15 | ″ | schema:isAccessibleForFree | false |
16 | ″ | schema:isPartOf | sg:journal.1041785 |
17 | ″ | schema:keywords | C1 |
18 | ″ | ″ | C2 |
19 | ″ | ″ | SFLA |
20 | ″ | ″ | ability |
21 | ″ | ″ | above shortcomings |
22 | ″ | ″ | accuracy |
23 | ″ | ″ | advantages |
24 | ″ | ″ | algorithm |
25 | ″ | ″ | applications |
26 | ″ | ″ | basic SFLA |
27 | ″ | ″ | best individual |
28 | ″ | ″ | better optimization performance |
29 | ″ | ″ | contraction factor |
30 | ″ | ″ | convergence |
31 | ″ | ″ | convergence of algorithm |
32 | ″ | ″ | convergence rate |
33 | ″ | ″ | design |
34 | ″ | ″ | entire population |
35 | ″ | ″ | factor C1 |
36 | ″ | ″ | factor χ |
37 | ″ | ″ | factors |
38 | ″ | ″ | frogs |
39 | ″ | ″ | full advantage |
40 | ″ | ″ | function problems |
41 | ″ | ″ | global best individual |
42 | ″ | ″ | improved SFLA |
43 | ″ | ″ | individuals |
44 | ″ | ″ | information |
45 | ″ | ″ | local optimization |
46 | ″ | ″ | local optimum |
47 | ″ | ″ | low optimization accuracy |
48 | ″ | ″ | mechanical design |
49 | ″ | ″ | operators |
50 | ″ | ″ | optimization |
51 | ″ | ″ | optimization accuracy |
52 | ″ | ″ | optimization performance |
53 | ″ | ″ | optimum |
54 | ″ | ″ | optimum design |
55 | ″ | ″ | performance |
56 | ″ | ″ | population |
57 | ″ | ″ | practicability |
58 | ″ | ″ | problem |
59 | ″ | ″ | rate |
60 | ″ | ″ | results |
61 | ″ | ″ | self-learning ability |
62 | ″ | ″ | self-learning operator |
63 | ″ | ″ | shortcomings |
64 | ″ | ″ | simulation results |
65 | ″ | ″ | useful information |
66 | ″ | ″ | validity |
67 | ″ | ″ | worst individuals |
68 | ″ | schema:name | A shuffled frog leaping algorithm with contraction factor and its application in mechanical optimum design |
69 | ″ | schema:pagination | 1-19 |
70 | ″ | schema:productId | N168004643985432f9b60e052ac6e76eb |
71 | ″ | ″ | Ndc634920786241058bf8558c7fed0331 |
72 | ″ | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1142376306 |
73 | ″ | ″ | https://doi.org/10.1007/s00366-021-01510-8 |
74 | ″ | schema:sdDatePublished | 2022-05-20T07:38 |
75 | ″ | schema:sdLicense | https://scigraph.springernature.com/explorer/license/ |
76 | ″ | schema:sdPublisher | Ne47be0a7953141e595e5a42728a4d854 |
77 | ″ | schema:url | https://doi.org/10.1007/s00366-021-01510-8 |
78 | ″ | sgo:license | sg:explorer/license/ |
79 | ″ | sgo:sdDataset | articles |
80 | ″ | rdf:type | schema:ScholarlyArticle |
81 | N168004643985432f9b60e052ac6e76eb | schema:name | doi |
82 | ″ | schema:value | 10.1007/s00366-021-01510-8 |
83 | ″ | rdf:type | schema:PropertyValue |
84 | N340c0c5b06d94628a1ca2b444dab229e | rdf:first | N761c230bee7b409594cdd768be029de6 |
85 | ″ | rdf:rest | rdf:nil |
86 | N761c230bee7b409594cdd768be029de6 | schema:affiliation | grid-institutes:grid.411734.4 |
87 | ″ | schema:familyName | Liu |
88 | ″ | schema:givenName | Xiaojuan |
89 | ″ | rdf:type | schema:Person |
90 | Ndc634920786241058bf8558c7fed0331 | schema:name | dimensions_id |
91 | ″ | schema:value | pub.1142376306 |
92 | ″ | rdf:type | schema:PropertyValue |
93 | Ne47be0a7953141e595e5a42728a4d854 | schema:name | Springer Nature - SN SciGraph project |
94 | ″ | rdf:type | schema:Organization |
95 | Ne80b7d3c551440fb9acf3d657624da42 | rdf:first | sg:person.011546457215.07 |
96 | ″ | rdf:rest | N340c0c5b06d94628a1ca2b444dab229e |
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:journal.1041785 | schema:issn | 0177-0667 |
104 | ″ | ″ | 1435-5663 |
105 | ″ | schema:name | Engineering with Computers |
106 | ″ | schema:publisher | Springer Nature |
107 | ″ | rdf:type | schema:Periodical |
108 | sg:person.011546457215.07 | schema:affiliation | grid-institutes:grid.411734.4 |
109 | ″ | schema:familyName | Wang |
110 | ″ | schema:givenName | Lianguo |
111 | ″ | schema:sameAs | https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011546457215.07 |
112 | ″ | rdf:type | schema:Person |
113 | sg:pub.10.1007/978-3-662-03423-1_27 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1011772001 |
114 | ″ | ″ | https://doi.org/10.1007/978-3-662-03423-1_27 |
115 | ″ | rdf:type | schema:CreativeWork |
116 | sg:pub.10.1007/s00170-016-9523-2 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1016842758 |
117 | ″ | ″ | https://doi.org/10.1007/s00170-016-9523-2 |
118 | ″ | rdf:type | schema:CreativeWork |
119 | sg:pub.10.1007/s00366-011-0241-y | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1009081493 |
120 | ″ | ″ | https://doi.org/10.1007/s00366-011-0241-y |
121 | ″ | rdf:type | schema:CreativeWork |
122 | sg:pub.10.1007/s00500-019-04484-4 | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1122284946 |
123 | ″ | ″ | https://doi.org/10.1007/s00500-019-04484-4 |
124 | ″ | rdf:type | schema:CreativeWork |
125 | sg:pub.10.1007/s10898-007-9149-x | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1049543869 |
126 | ″ | ″ | https://doi.org/10.1007/s10898-007-9149-x |
127 | ″ | rdf:type | schema:CreativeWork |
128 | sg:pub.10.1007/s11831-019-09343-x | schema:sameAs | https://app.dimensions.ai/details/publication/pub.1115676632 |
129 | ″ | ″ | https://doi.org/10.1007/s11831-019-09343-x |
130 | ″ | rdf:type | schema:CreativeWork |
131 | grid-institutes:grid.411734.4 | schema:alternateName | College of Information Science and Technology, Gansu Agricultural University, No. 1 Yingmen Village, 730070, Lanzhou, Gansu, China |
132 | ″ | schema:name | College of Information Science and Technology, Gansu Agricultural University, No. 1 Yingmen Village, 730070, Lanzhou, Gansu, China |
133 | ″ | rdf:type | schema:Organization |