Particle Swarm Optimization Based on Dynamic Island Model
Particle Swarm Optimization (PSO) algorithm is a metaheuristic that has been used for solving optimization problems. In this method, many modifications have been carried out in order to improve the search performance. Furthermore, island models is a structured population mechanism used to preserve t...
Saved in:
Published in | 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) pp. 709 - 716 |
---|---|
Main Authors | , , |
Format | Conference Proceeding |
Language | English |
Published |
IEEE
01.11.2017
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Abstract | Particle Swarm Optimization (PSO) algorithm is a metaheuristic that has been used for solving optimization problems. In this method, many modifications have been carried out in order to improve the search performance. Furthermore, island models is a structured population mechanism used to preserve the diversity and thus to improve the population performance. The aim of this paper is to integrate dynamic island models with PSO algorithm to improve its convergence and its diversity properties where the new method is referred to as island PSO. The dynamic regulation of migrations aims to distribute the particles in the search space. The experimental results, using a set of benchmark functions show that the island model context is crucial to the PSO performance and the comparative study shows the efficiency of the integration of dynamic island models. |
---|---|
AbstractList | Particle Swarm Optimization (PSO) algorithm is a metaheuristic that has been used for solving optimization problems. In this method, many modifications have been carried out in order to improve the search performance. Furthermore, island models is a structured population mechanism used to preserve the diversity and thus to improve the population performance. The aim of this paper is to integrate dynamic island models with PSO algorithm to improve its convergence and its diversity properties where the new method is referred to as island PSO. The dynamic regulation of migrations aims to distribute the particles in the search space. The experimental results, using a set of benchmark functions show that the island model context is crucial to the PSO performance and the comparative study shows the efficiency of the integration of dynamic island models. |
Author | Smairi, Nadia Abadlia, Houda Ghedira, Khaled |
Author_xml | – sequence: 1 givenname: Houda surname: Abadlia fullname: Abadlia, Houda – sequence: 2 givenname: Nadia surname: Smairi fullname: Smairi, Nadia – sequence: 3 givenname: Khaled surname: Ghedira fullname: Ghedira, Khaled |
BookMark | eNotj8tKw0AUQEdRsKnuBTfzA4lz53ZeyxpfgUoF67rcyUxgJI-SBKR-vQVdnbM6cDJ20Q99ZOwWRAEg3H1V7tZVIQWYQggAPGMZKLQardHynC0kGpULcOaKZdP0JYQUSuKCuXca51S3kX9809jx7WFOXfqhOQ09f6ApBn6Sx2NPXap5NbXUB_42hNhes8uG2ine_HPJPp-fduVrvtm-VOV6kycwas7RIQUikOhVJFUDgLXQkI_gvIkhGEVN0MJK58EgaA9Ca7uyKxW1NxaX7O6vm2KM-8OYOhqPe4vmNKvxFyzoRwM |
CODEN | IEEPAD |
ContentType | Conference Proceeding |
DBID | 6IE 6IH CBEJK RIE RIO |
DOI | 10.1109/ICTAI.2017.00113 |
DatabaseName | IEEE Electronic Library (IEL) Conference Proceedings IEEE Proceedings Order Plan (POP) 1998-present by volume IEEE Xplore All Conference Proceedings IEEE Xplore IEEE Proceedings Order Plans (POP) 1998-present |
DatabaseTitleList | |
Database_xml | – sequence: 1 dbid: RIE name: IEEE Xplore url: https://proxy.k.utb.cz/login?url=https://ieeexplore.ieee.org/ sourceTypes: Publisher |
DeliveryMethod | fulltext_linktorsrc |
Discipline | Statistics Computer Science |
EISBN | 1538638762 9781538638767 |
EISSN | 2375-0197 |
EndPage | 716 |
ExternalDocumentID | 8372016 |
Genre | orig-research |
GroupedDBID | 23M 29O 6IE 6IF 6IH 6IK 6IL 6IN ABLEC ACGFS ADZIZ ALMA_UNASSIGNED_HOLDINGS BEFXN BFFAM BGNUA BKEBE BPEOZ CBEJK CHZPO IEGSK IJVOP JC5 M43 OCL RIE RIL RIO |
ID | FETCH-LOGICAL-i175t-393adaa123b5ea5c111881fabe19b7edd75afd60829b17316b106684845e6b783 |
IEDL.DBID | RIE |
IngestDate | Wed Jun 26 19:28:48 EDT 2024 |
IsPeerReviewed | false |
IsScholarly | false |
Language | English |
LinkModel | DirectLink |
MergedId | FETCHMERGED-LOGICAL-i175t-393adaa123b5ea5c111881fabe19b7edd75afd60829b17316b106684845e6b783 |
PageCount | 8 |
ParticipantIDs | ieee_primary_8372016 |
PublicationCentury | 2000 |
PublicationDate | 2017-Nov |
PublicationDateYYYYMMDD | 2017-11-01 |
PublicationDate_xml | – month: 11 year: 2017 text: 2017-Nov |
PublicationDecade | 2010 |
PublicationTitle | 2017 IEEE 29th International Conference on Tools with Artificial Intelligence (ICTAI) |
PublicationTitleAbbrev | TAI |
PublicationYear | 2017 |
Publisher | IEEE |
Publisher_xml | – name: IEEE |
SSID | ssj0020523 ssj0002683188 |
Score | 1.7359421 |
Snippet | Particle Swarm Optimization (PSO) algorithm is a metaheuristic that has been used for solving optimization problems. In this method, many modifications have... |
SourceID | ieee |
SourceType | Publisher |
StartPage | 709 |
SubjectTerms | Diversity Dynamic Island Model Heuristic algorithms Linear programming Migration Optimization Particle swarm optimization Sociology Statistics Topology |
Title | Particle Swarm Optimization Based on Dynamic Island Model |
URI | https://ieeexplore.ieee.org/document/8372016 |
hasFullText | 1 |
inHoldings | 1 |
isFullTextHit | |
isPrint | |
link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3fS8MwED7mnvY03Sb-Jg8-2m1t2jR91OnYBHXgBnsbSXMB0XUyNgT_ei9tV1F88C0EUkJyvbvc3XcfwKUQOrT0DvBkYqwXKoNeIiz3LHId9y2JiMqrfB_FaBbez6N5Da4qLAwi5sVn2HXDPJdvVunWhcp60lGq-GIP9mQ_KLBaVTwlEJLEs8ogBC7cuUtL9pPeeDC9HrtKrjjPPfAfZCq5LRk24WG3i6KE5LW73ehu-vmrQeN_t7kPnW_UHptU9ugAapi1oLmjbWDlX9yChnMwi_7MbUgmpeyw5w-1XrInUiHLEpvJbsjEGUaD24K3njkBygxzBGpvHZgN76aDkVfSKXgv5CNsPJ5wZZQiU6UjVFFKWk5K3yqNfqJjNCaOlDXCgW217witND0XhQxlGKHQseSHUM9WGR4BI22dKEHOSYg85NKqgFaQ55NaY-nDeAxtdyyL96JjxqI8kZO_p0-h4S6mQPidQX2z3uI5mfqNvsjv-AvypKdN |
link.rule.ids | 310,311,786,790,795,796,802,27956,55107 |
linkProvider | IEEE |
linkToHtml | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwjV3PT8IwFH5BPMgJBYy_7cGjA0a3rjsqSkABSYSEG2nXNjHKMGTExL_e123MaDx4a5Zsadq39732ve99AFeMSc_gOcDhoTKOJ5R2QmaoYzSVQdugiYi0ynfM-jPvYe7PS3BdcGG01mnxmW7aYZrLV6toY6_KWtxKqrhsB3YR59tBxtYqblQ6jKOBFjmEjr3w3CYm22Fr0J3eDGwtV5BmH-gPOZUUTXpVGG3nkRWRvDY3iWxGn79aNP53ovvQ-ObtkUmBSAdQ0nENqlvhBpL_xzWo2BAz69Bch3CSWw95_hDrJXlCJ7LM2ZnkFkFOERzcZcr1xJpQrIiVUHtrwKx3P-32nVxQwXnBKCFxaEiFEgLBSvpa-BH6Oc5dI6R2QxlopQJfGMUs3Va6VtJK4oGRcY97vmYy4PQQyvEq1kdA0F-HgmF44mnqUW5EB9_A2CcyyuCH9THU7bIs3rOeGYt8RU7-fnwJe_3paLgYDsaPp1Cxm5Tx_c6gnKw3-hyBP5EX6X5_AfXwqqE |
openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Abook&rft.genre=proceeding&rft.title=2017+IEEE+29th+International+Conference+on+Tools+with+Artificial+Intelligence+%28ICTAI%29&rft.atitle=Particle+Swarm+Optimization+Based+on+Dynamic+Island+Model&rft.au=Abadlia%2C+Houda&rft.au=Smairi%2C+Nadia&rft.au=Ghedira%2C+Khaled&rft.date=2017-11-01&rft.pub=IEEE&rft.eissn=2375-0197&rft.spage=709&rft.epage=716&rft_id=info:doi/10.1109%2FICTAI.2017.00113&rft.externalDocID=8372016 |