No Cover Image

Journal article 535 views 179 downloads

A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm

Ivona Brajević, Predrag S. Stanimirović, Shuai Li Orcid Logo, Xinwei Cao

International Journal of Computational Intelligence Systems, Volume: 13, Issue: 1, Pages: 810 - 821

Swansea University Author: Shuai Li Orcid Logo

  • 54989.pdf

    PDF | Version of Record

    This is an open access article distributed under the CC BY-NC 4.0 license.

    Download (2.17MB)

Check full text

DOI (Published version): 10.2991/ijcis.d.200612.001

Abstract

Many hard optimization problems have been efficiently solved by two notable swarm intelligence algorithms, artificial bee colony (ABC) and firefly algorithm (FA). In this paper, a collaborative hybrid algorithm based on firefly and multi-strategy artificial bee colony, abbreviated as FA-MABC, is pro...

Full description

Published in: International Journal of Computational Intelligence Systems
ISSN: 1875-6891 1875-6883
Published: Atlantis Press 2020
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa54989
Tags: Add Tag
No Tags, Be the first to tag this record!
first_indexed 2020-08-17T09:05:39Z
last_indexed 2020-09-17T03:19:01Z
id cronfa54989
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2020-09-16T16:29:22.9224527</datestamp><bib-version>v2</bib-version><id>54989</id><entry>2020-08-17</entry><title>A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm</title><swanseaauthors><author><sid>42ff9eed09bcd109fbbe484a0f99a8a8</sid><ORCID>0000-0001-8316-5289</ORCID><firstname>Shuai</firstname><surname>Li</surname><name>Shuai Li</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2020-08-17</date><deptcode>MECH</deptcode><abstract>Many hard optimization problems have been efficiently solved by two notable swarm intelligence algorithms, artificial bee colony (ABC) and firefly algorithm (FA). In this paper, a collaborative hybrid algorithm based on firefly and multi-strategy artificial bee colony, abbreviated as FA-MABC, is proposed for solving single-objective optimization problems. In the proposed algorithm, FA investigates the search space globally to locate favorable regions of convergence. A novel multi-strategy ABC is employed to perform local search. The proposed algorithm incorporates a diversity measure to help in the switch criteria. The FA-MABC is tested on 40 benchmark functions with diverse complexities. Comparative results with the basic FA, ABC and other recent state-of-the-art metaheuristic algorithms demonstrate the competitive performance of the FA-MABC.</abstract><type>Journal Article</type><journal>International Journal of Computational Intelligence Systems</journal><volume>13</volume><journalNumber>1</journalNumber><paginationStart>810</paginationStart><paginationEnd>821</paginationEnd><publisher>Atlantis Press</publisher><issnPrint>1875-6891</issnPrint><issnElectronic>1875-6883</issnElectronic><keywords>Firefly algorithm, Artificial bee colony, Multi-strategy, Hybrid algorithm, Global optimization</keywords><publishedDay>23</publishedDay><publishedMonth>6</publishedMonth><publishedYear>2020</publishedYear><publishedDate>2020-06-23</publishedDate><doi>10.2991/ijcis.d.200612.001</doi><url/><notes/><college>COLLEGE NANME</college><department>Mechanical Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MECH</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2020-09-16T16:29:22.9224527</lastEdited><Created>2020-08-17T10:02:56.3193654</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering</level></path><authors><author><firstname>Ivona</firstname><surname>Brajevi&#x107;</surname><order>1</order></author><author><firstname>Predrag S.</firstname><surname>Stanimirovi&#x107;</surname><order>2</order></author><author><firstname>Shuai</firstname><surname>Li</surname><orcid>0000-0001-8316-5289</orcid><order>3</order></author><author><firstname>Xinwei</firstname><surname>Cao</surname><order>4</order></author></authors><documents><document><filename>54989__17943__92263a24f6b64e4fb5a8c9bb1f67e741.pdf</filename><originalFilename>54989.pdf</originalFilename><uploaded>2020-08-17T10:05:23.6042150</uploaded><type>Output</type><contentLength>2273353</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>This is an open access article distributed under the CC BY-NC 4.0 license.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>English</language><licence>http://creativecommons.org/licenses/by-nc/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2020-09-16T16:29:22.9224527 v2 54989 2020-08-17 A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm 42ff9eed09bcd109fbbe484a0f99a8a8 0000-0001-8316-5289 Shuai Li Shuai Li true false 2020-08-17 MECH Many hard optimization problems have been efficiently solved by two notable swarm intelligence algorithms, artificial bee colony (ABC) and firefly algorithm (FA). In this paper, a collaborative hybrid algorithm based on firefly and multi-strategy artificial bee colony, abbreviated as FA-MABC, is proposed for solving single-objective optimization problems. In the proposed algorithm, FA investigates the search space globally to locate favorable regions of convergence. A novel multi-strategy ABC is employed to perform local search. The proposed algorithm incorporates a diversity measure to help in the switch criteria. The FA-MABC is tested on 40 benchmark functions with diverse complexities. Comparative results with the basic FA, ABC and other recent state-of-the-art metaheuristic algorithms demonstrate the competitive performance of the FA-MABC. Journal Article International Journal of Computational Intelligence Systems 13 1 810 821 Atlantis Press 1875-6891 1875-6883 Firefly algorithm, Artificial bee colony, Multi-strategy, Hybrid algorithm, Global optimization 23 6 2020 2020-06-23 10.2991/ijcis.d.200612.001 COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2020-09-16T16:29:22.9224527 2020-08-17T10:02:56.3193654 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering Ivona Brajević 1 Predrag S. Stanimirović 2 Shuai Li 0000-0001-8316-5289 3 Xinwei Cao 4 54989__17943__92263a24f6b64e4fb5a8c9bb1f67e741.pdf 54989.pdf 2020-08-17T10:05:23.6042150 Output 2273353 application/pdf Version of Record true This is an open access article distributed under the CC BY-NC 4.0 license. true English http://creativecommons.org/licenses/by-nc/4.0/
title A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
spellingShingle A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
Shuai Li
title_short A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
title_full A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
title_fullStr A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
title_full_unstemmed A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
title_sort A Hybrid Firefly and Multi-Strategy Artificial Bee Colony Algorithm
author_id_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8
author_id_fullname_str_mv 42ff9eed09bcd109fbbe484a0f99a8a8_***_Shuai Li
author Shuai Li
author2 Ivona Brajević
Predrag S. Stanimirović
Shuai Li
Xinwei Cao
format Journal article
container_title International Journal of Computational Intelligence Systems
container_volume 13
container_issue 1
container_start_page 810
publishDate 2020
institution Swansea University
issn 1875-6891
1875-6883
doi_str_mv 10.2991/ijcis.d.200612.001
publisher Atlantis Press
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering
document_store_str 1
active_str 0
description Many hard optimization problems have been efficiently solved by two notable swarm intelligence algorithms, artificial bee colony (ABC) and firefly algorithm (FA). In this paper, a collaborative hybrid algorithm based on firefly and multi-strategy artificial bee colony, abbreviated as FA-MABC, is proposed for solving single-objective optimization problems. In the proposed algorithm, FA investigates the search space globally to locate favorable regions of convergence. A novel multi-strategy ABC is employed to perform local search. The proposed algorithm incorporates a diversity measure to help in the switch criteria. The FA-MABC is tested on 40 benchmark functions with diverse complexities. Comparative results with the basic FA, ABC and other recent state-of-the-art metaheuristic algorithms demonstrate the competitive performance of the FA-MABC.
published_date 2020-06-23T04:08:53Z
_version_ 1763753626326532096
score 11.012678

Similar Items