A shuffled frog leaping algorithm with contraction factor and its application in mechanical optimum design View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2021-11-03

AUTHORS

Lianguo Wang, Xiaojuan Liu

ABSTRACT

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. More... »

PAGES

1-19

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00366-021-01510-8

DOI

http://dx.doi.org/10.1007/s00366-021-01510-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1142376306


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": "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

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/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
 




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


...