A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms
Abstract Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for ev...
Ausführliche Beschreibung
Autor*in: |
Phan, Han Duy [verfasserIn] |
---|
Format: |
Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2019 |
---|
Schlagwörter: |
---|
Anmerkung: |
© Springer-Verlag London Ltd., part of Springer Nature 2019 |
---|
Übergeordnetes Werk: |
Enthalten in: Neural computing & applications - Springer London, 1993, 32(2019), 2 vom: 20. Mai, Seite 567-588 |
---|---|
Übergeordnetes Werk: |
volume:32 ; year:2019 ; number:2 ; day:20 ; month:05 ; pages:567-588 |
Links: |
---|
DOI / URN: |
10.1007/s00521-019-04229-2 |
---|
Katalog-ID: |
OLC2025616759 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | OLC2025616759 | ||
003 | DE-627 | ||
005 | 20230502114903.0 | ||
007 | tu | ||
008 | 200820s2019 xx ||||| 00| ||eng c | ||
024 | 7 | |a 10.1007/s00521-019-04229-2 |2 doi | |
035 | |a (DE-627)OLC2025616759 | ||
035 | |a (DE-He213)s00521-019-04229-2-p | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 004 |q VZ |
100 | 1 | |a Phan, Han Duy |e verfasserin |0 (orcid)0000-0001-8138-9211 |4 aut | |
245 | 1 | 0 | |a A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms |
264 | 1 | |c 2019 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
338 | |a Band |b nc |2 rdacarrier | ||
500 | |a © Springer-Verlag London Ltd., part of Springer Nature 2019 | ||
520 | |a Abstract Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms. | ||
650 | 4 | |a Swarm intelligence algorithms | |
650 | 4 | |a Dynamic parameter setting | |
650 | 4 | |a Parameter control | |
700 | 1 | |a Ellis, Kirsten |0 (orcid)0000-0002-7570-0939 |4 aut | |
700 | 1 | |a Barca, Jan Carlo |0 (orcid)0000-0001-6939-4632 |4 aut | |
700 | 1 | |a Dorin, Alan |0 (orcid)0000-0002-5456-4835 |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Neural computing & applications |d Springer London, 1993 |g 32(2019), 2 vom: 20. Mai, Seite 567-588 |w (DE-627)165669608 |w (DE-600)1136944-9 |w (DE-576)032873050 |x 0941-0643 |7 nnns |
773 | 1 | 8 | |g volume:32 |g year:2019 |g number:2 |g day:20 |g month:05 |g pages:567-588 |
856 | 4 | 1 | |u https://doi.org/10.1007/s00521-019-04229-2 |z lizenzpflichtig |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_OLC | ||
912 | |a SSG-OLC-MAT | ||
912 | |a GBV_ILN_70 | ||
912 | |a GBV_ILN_2018 | ||
912 | |a GBV_ILN_4277 | ||
951 | |a AR | ||
952 | |d 32 |j 2019 |e 2 |b 20 |c 05 |h 567-588 |
author_variant |
h d p hd hdp k e ke j c b jc jcb a d ad |
---|---|
matchkey_str |
article:09410643:2019----::sreodnmcaaeestigehdfrauenprdwr |
hierarchy_sort_str |
2019 |
publishDate |
2019 |
allfields |
10.1007/s00521-019-04229-2 doi (DE-627)OLC2025616759 (DE-He213)s00521-019-04229-2-p DE-627 ger DE-627 rakwb eng 004 VZ Phan, Han Duy verfasserin (orcid)0000-0001-8138-9211 aut A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms. Swarm intelligence algorithms Dynamic parameter setting Parameter control Ellis, Kirsten (orcid)0000-0002-7570-0939 aut Barca, Jan Carlo (orcid)0000-0001-6939-4632 aut Dorin, Alan (orcid)0000-0002-5456-4835 aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 2 vom: 20. Mai, Seite 567-588 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:2 day:20 month:05 pages:567-588 https://doi.org/10.1007/s00521-019-04229-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 2 20 05 567-588 |
spelling |
10.1007/s00521-019-04229-2 doi (DE-627)OLC2025616759 (DE-He213)s00521-019-04229-2-p DE-627 ger DE-627 rakwb eng 004 VZ Phan, Han Duy verfasserin (orcid)0000-0001-8138-9211 aut A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms. Swarm intelligence algorithms Dynamic parameter setting Parameter control Ellis, Kirsten (orcid)0000-0002-7570-0939 aut Barca, Jan Carlo (orcid)0000-0001-6939-4632 aut Dorin, Alan (orcid)0000-0002-5456-4835 aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 2 vom: 20. Mai, Seite 567-588 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:2 day:20 month:05 pages:567-588 https://doi.org/10.1007/s00521-019-04229-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 2 20 05 567-588 |
allfields_unstemmed |
10.1007/s00521-019-04229-2 doi (DE-627)OLC2025616759 (DE-He213)s00521-019-04229-2-p DE-627 ger DE-627 rakwb eng 004 VZ Phan, Han Duy verfasserin (orcid)0000-0001-8138-9211 aut A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms. Swarm intelligence algorithms Dynamic parameter setting Parameter control Ellis, Kirsten (orcid)0000-0002-7570-0939 aut Barca, Jan Carlo (orcid)0000-0001-6939-4632 aut Dorin, Alan (orcid)0000-0002-5456-4835 aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 2 vom: 20. Mai, Seite 567-588 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:2 day:20 month:05 pages:567-588 https://doi.org/10.1007/s00521-019-04229-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 2 20 05 567-588 |
allfieldsGer |
10.1007/s00521-019-04229-2 doi (DE-627)OLC2025616759 (DE-He213)s00521-019-04229-2-p DE-627 ger DE-627 rakwb eng 004 VZ Phan, Han Duy verfasserin (orcid)0000-0001-8138-9211 aut A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms. Swarm intelligence algorithms Dynamic parameter setting Parameter control Ellis, Kirsten (orcid)0000-0002-7570-0939 aut Barca, Jan Carlo (orcid)0000-0001-6939-4632 aut Dorin, Alan (orcid)0000-0002-5456-4835 aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 2 vom: 20. Mai, Seite 567-588 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:2 day:20 month:05 pages:567-588 https://doi.org/10.1007/s00521-019-04229-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 2 20 05 567-588 |
allfieldsSound |
10.1007/s00521-019-04229-2 doi (DE-627)OLC2025616759 (DE-He213)s00521-019-04229-2-p DE-627 ger DE-627 rakwb eng 004 VZ Phan, Han Duy verfasserin (orcid)0000-0001-8138-9211 aut A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © Springer-Verlag London Ltd., part of Springer Nature 2019 Abstract Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms. Swarm intelligence algorithms Dynamic parameter setting Parameter control Ellis, Kirsten (orcid)0000-0002-7570-0939 aut Barca, Jan Carlo (orcid)0000-0001-6939-4632 aut Dorin, Alan (orcid)0000-0002-5456-4835 aut Enthalten in Neural computing & applications Springer London, 1993 32(2019), 2 vom: 20. Mai, Seite 567-588 (DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 0941-0643 nnns volume:32 year:2019 number:2 day:20 month:05 pages:567-588 https://doi.org/10.1007/s00521-019-04229-2 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 AR 32 2019 2 20 05 567-588 |
language |
English |
source |
Enthalten in Neural computing & applications 32(2019), 2 vom: 20. Mai, Seite 567-588 volume:32 year:2019 number:2 day:20 month:05 pages:567-588 |
sourceStr |
Enthalten in Neural computing & applications 32(2019), 2 vom: 20. Mai, Seite 567-588 volume:32 year:2019 number:2 day:20 month:05 pages:567-588 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Swarm intelligence algorithms Dynamic parameter setting Parameter control |
dewey-raw |
004 |
isfreeaccess_bool |
false |
container_title |
Neural computing & applications |
authorswithroles_txt_mv |
Phan, Han Duy @@aut@@ Ellis, Kirsten @@aut@@ Barca, Jan Carlo @@aut@@ Dorin, Alan @@aut@@ |
publishDateDaySort_date |
2019-05-20T00:00:00Z |
hierarchy_top_id |
165669608 |
dewey-sort |
14 |
id |
OLC2025616759 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2025616759</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502114903.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2019 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-019-04229-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2025616759</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00521-019-04229-2-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Phan, Han Duy</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-8138-9211</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London Ltd., part of Springer Nature 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Swarm intelligence algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dynamic parameter setting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Parameter control</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ellis, Kirsten</subfield><subfield code="0">(orcid)0000-0002-7570-0939</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Barca, Jan Carlo</subfield><subfield code="0">(orcid)0000-0001-6939-4632</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dorin, Alan</subfield><subfield code="0">(orcid)0000-0002-5456-4835</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">Springer London, 1993</subfield><subfield code="g">32(2019), 2 vom: 20. Mai, Seite 567-588</subfield><subfield code="w">(DE-627)165669608</subfield><subfield code="w">(DE-600)1136944-9</subfield><subfield code="w">(DE-576)032873050</subfield><subfield code="x">0941-0643</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:32</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:2</subfield><subfield code="g">day:20</subfield><subfield code="g">month:05</subfield><subfield code="g">pages:567-588</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00521-019-04229-2</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">32</subfield><subfield code="j">2019</subfield><subfield code="e">2</subfield><subfield code="b">20</subfield><subfield code="c">05</subfield><subfield code="h">567-588</subfield></datafield></record></collection>
|
author |
Phan, Han Duy |
spellingShingle |
Phan, Han Duy ddc 004 misc Swarm intelligence algorithms misc Dynamic parameter setting misc Parameter control A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms |
authorStr |
Phan, Han Duy |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)165669608 |
format |
Article |
dewey-ones |
004 - Data processing & computer science |
delete_txt_mv |
keep |
author_role |
aut aut aut aut |
collection |
OLC |
remote_str |
false |
illustrated |
Not Illustrated |
issn |
0941-0643 |
topic_title |
004 VZ A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms Swarm intelligence algorithms Dynamic parameter setting Parameter control |
topic |
ddc 004 misc Swarm intelligence algorithms misc Dynamic parameter setting misc Parameter control |
topic_unstemmed |
ddc 004 misc Swarm intelligence algorithms misc Dynamic parameter setting misc Parameter control |
topic_browse |
ddc 004 misc Swarm intelligence algorithms misc Dynamic parameter setting misc Parameter control |
format_facet |
Aufsätze Gedruckte Aufsätze |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
nc |
hierarchy_parent_title |
Neural computing & applications |
hierarchy_parent_id |
165669608 |
dewey-tens |
000 - Computer science, knowledge & systems |
hierarchy_top_title |
Neural computing & applications |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)165669608 (DE-600)1136944-9 (DE-576)032873050 |
title |
A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms |
ctrlnum |
(DE-627)OLC2025616759 (DE-He213)s00521-019-04229-2-p |
title_full |
A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms |
author_sort |
Phan, Han Duy |
journal |
Neural computing & applications |
journalStr |
Neural computing & applications |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
000 - Computer science, information & general works |
recordtype |
marc |
publishDateSort |
2019 |
contenttype_str_mv |
txt |
container_start_page |
567 |
author_browse |
Phan, Han Duy Ellis, Kirsten Barca, Jan Carlo Dorin, Alan |
container_volume |
32 |
class |
004 VZ |
format_se |
Aufsätze |
author-letter |
Phan, Han Duy |
doi_str_mv |
10.1007/s00521-019-04229-2 |
normlink |
(ORCID)0000-0001-8138-9211 (ORCID)0000-0002-7570-0939 (ORCID)0000-0001-6939-4632 (ORCID)0000-0002-5456-4835 |
normlink_prefix_str_mv |
(orcid)0000-0001-8138-9211 (orcid)0000-0002-7570-0939 (orcid)0000-0001-6939-4632 (orcid)0000-0002-5456-4835 |
dewey-full |
004 |
title_sort |
a survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms |
title_auth |
A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms |
abstract |
Abstract Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
abstractGer |
Abstract Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
abstract_unstemmed |
Abstract Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms. © Springer-Verlag London Ltd., part of Springer Nature 2019 |
collection_details |
GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT GBV_ILN_70 GBV_ILN_2018 GBV_ILN_4277 |
container_issue |
2 |
title_short |
A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms |
url |
https://doi.org/10.1007/s00521-019-04229-2 |
remote_bool |
false |
author2 |
Ellis, Kirsten Barca, Jan Carlo Dorin, Alan |
author2Str |
Ellis, Kirsten Barca, Jan Carlo Dorin, Alan |
ppnlink |
165669608 |
mediatype_str_mv |
n |
isOA_txt |
false |
hochschulschrift_bool |
false |
doi_str |
10.1007/s00521-019-04229-2 |
up_date |
2024-07-04T01:43:11.920Z |
_version_ |
1803610899790954496 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2025616759</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230502114903.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200820s2019 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00521-019-04229-2</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2025616759</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00521-019-04229-2-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">004</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Phan, Han Duy</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0001-8138-9211</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">A survey of dynamic parameter setting methods for nature-inspired swarm intelligence algorithms</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© Springer-Verlag London Ltd., part of Springer Nature 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Parameter settings for nature-inspired optimization algorithms are essential for their effective performance. Evolutionary algorithms and swarm intelligence algorithms are prominent types of nature-inspired optimization. There are comprehensive reviews of parameter setting techniques for evolutionary algorithms. Counterparts providing an overview of parameter setting techniques for swarm intelligence algorithms are needed also. Therefore, in this paper, we provide a critical and comprehensive review, focusing in particular on dynamic parameter setting techniques. The paper describes a variety of swarm intelligence algorithms and parameter setting approaches that have been applied to them. This review simplifies the selection of parameter setting techniques for each algorithm by collecting them in a single document and classifying them under a taxonomy. Recommendations for parameter setting approach selection are provided in this review. We explore the open problems related to dynamic parameter setting techniques for swarm intelligence optimization and discuss the trade-off between run-time computation and flexibility of these algorithms.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Swarm intelligence algorithms</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Dynamic parameter setting</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Parameter control</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ellis, Kirsten</subfield><subfield code="0">(orcid)0000-0002-7570-0939</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Barca, Jan Carlo</subfield><subfield code="0">(orcid)0000-0001-6939-4632</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Dorin, Alan</subfield><subfield code="0">(orcid)0000-0002-5456-4835</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Neural computing & applications</subfield><subfield code="d">Springer London, 1993</subfield><subfield code="g">32(2019), 2 vom: 20. Mai, Seite 567-588</subfield><subfield code="w">(DE-627)165669608</subfield><subfield code="w">(DE-600)1136944-9</subfield><subfield code="w">(DE-576)032873050</subfield><subfield code="x">0941-0643</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:32</subfield><subfield code="g">year:2019</subfield><subfield code="g">number:2</subfield><subfield code="g">day:20</subfield><subfield code="g">month:05</subfield><subfield code="g">pages:567-588</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00521-019-04229-2</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_70</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_4277</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">32</subfield><subfield code="j">2019</subfield><subfield code="e">2</subfield><subfield code="b">20</subfield><subfield code="c">05</subfield><subfield code="h">567-588</subfield></datafield></record></collection>
|
score |
7.3995066 |