Cooperative Group Search Optimization

Group Search Optimizer (GSO) is a population-based optimization approach inspired by animal searching behaviour and group living theory. Although competition among population members may improve their performance, greater improvements could be achieved through cooperation. In this paper, a new algor...

Full description

Saved in:
Bibliographic Details
Main Authors: Pacifico, L. D. S., Ludermir, T. B.
Format: Conference Proceeding
Language:English
Subjects:
Animals
Cooperative learning
Evolutionary computing
Genetic algorithms
Group search optimization
Measurement
Optimization
Sociology
Statistics
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by
cites
container_end_page 3306
container_issue
container_start_page 3299
container_title
container_volume
creator Pacifico, L. D. S.
Ludermir, T. B.
description Group Search Optimizer (GSO) is a population-based optimization approach inspired by animal searching behaviour and group living theory. Although competition among population members may improve their performance, greater improvements could be achieved through cooperation. In this paper, a new algorithm is presented, called Cooperative Group Search Optimizer (CGSO), based on divide-and-conquer paradigm, employing cooperative behaviour among multiple GSO groups to improve the performance of standard GSO. Nine benchmark functions are used to evaluate the performance of the proposed technique. Experimental results show that the CGSO approach is able to achieve better results than standard GSO in most of the tested problems.
doi_str_mv 10.1109/CEC.2013.6557974
format conference_proceeding
fullrecord <record><control><sourceid>ieee_CHZPO</sourceid><recordid>TN_cdi_ieee_primary_6557974</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><ieee_id>6557974</ieee_id><sourcerecordid>6557974</sourcerecordid><originalsourceid>FETCH-LOGICAL-i175t-69a6b49c592d9b43599ca464ef6508d2fbe0f7511a585f9959dae3dbaf2041f83</originalsourceid><addsrcrecordid>eNpFz89LwzAYxvH4C5xzd8FLLx5b3zfJm-Q9SplTGOygwm4jbROMOFvaKuhfb8GBp-fwgS88QlwhFIjAt-WyLCSgKgyRZauPxAVqywya5PZYzJA15gDSnPyDcqcTgOPcWrc9F4theAOAqWcB1EzclG3bhd6P6Stkq7797LKn4Pv6Ndt0Y9qnn0naj0txFv37EBaHnYuX--Vz-ZCvN6vH8m6dJ7Q05oa9qTTXxLLhSitirr02OkRD4BoZqwDREqInR5GZuPFBNZWPEjRGp-bi-q-bQgi7rk9733_vDnfVLy64Q6M</addsrcrecordid><sourcetype>Publisher</sourcetype><iscdi>true</iscdi><recordtype>conference_proceeding</recordtype></control><display><type>conference_proceeding</type><title>Cooperative Group Search Optimization</title><source>IEEE Xplore All Conference Series</source><creator>Pacifico, L. D. S. ; Ludermir, T. B.</creator><creatorcontrib>Pacifico, L. D. S. ; Ludermir, T. B.</creatorcontrib><description>Group Search Optimizer (GSO) is a population-based optimization approach inspired by animal searching behaviour and group living theory. Although competition among population members may improve their performance, greater improvements could be achieved through cooperation. In this paper, a new algorithm is presented, called Cooperative Group Search Optimizer (CGSO), based on divide-and-conquer paradigm, employing cooperative behaviour among multiple GSO groups to improve the performance of standard GSO. Nine benchmark functions are used to evaluate the performance of the proposed technique. Experimental results show that the CGSO approach is able to achieve better results than standard GSO in most of the tested problems.</description><identifier>ISSN: 1089-778X</identifier><identifier>ISBN: 1479904538</identifier><identifier>ISBN: 9781479904532</identifier><identifier>EISSN: 1941-0026</identifier><identifier>EISBN: 147990452X</identifier><identifier>EISBN: 9781479904525</identifier><identifier>EISBN: 9781479904549</identifier><identifier>EISBN: 1479904546</identifier><identifier>EISBN: 9781479904518</identifier><identifier>EISBN: 1479904511</identifier><identifier>DOI: 10.1109/CEC.2013.6557974</identifier><language>eng</language><publisher>IEEE</publisher><subject>Animals ; Cooperative learning ; Evolutionary computing ; Genetic algorithms ; Group search optimization ; Measurement ; Optimization ; Sociology ; Statistics</subject><ispartof>2013 IEEE Congress on Evolutionary Computation, 2013, p.3299-3306</ispartof><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/6557974$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2056,27923,54553,54794,54918,54930</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/6557974$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Pacifico, L. D. S.</creatorcontrib><creatorcontrib>Ludermir, T. B.</creatorcontrib><title>Cooperative Group Search Optimization</title><title>2013 IEEE Congress on Evolutionary Computation</title><addtitle>CEC</addtitle><description>Group Search Optimizer (GSO) is a population-based optimization approach inspired by animal searching behaviour and group living theory. Although competition among population members may improve their performance, greater improvements could be achieved through cooperation. In this paper, a new algorithm is presented, called Cooperative Group Search Optimizer (CGSO), based on divide-and-conquer paradigm, employing cooperative behaviour among multiple GSO groups to improve the performance of standard GSO. Nine benchmark functions are used to evaluate the performance of the proposed technique. Experimental results show that the CGSO approach is able to achieve better results than standard GSO in most of the tested problems.</description><subject>Animals</subject><subject>Cooperative learning</subject><subject>Evolutionary computing</subject><subject>Genetic algorithms</subject><subject>Group search optimization</subject><subject>Measurement</subject><subject>Optimization</subject><subject>Sociology</subject><subject>Statistics</subject><issn>1089-778X</issn><issn>1941-0026</issn><isbn>1479904538</isbn><isbn>9781479904532</isbn><isbn>147990452X</isbn><isbn>9781479904525</isbn><isbn>9781479904549</isbn><isbn>1479904546</isbn><isbn>9781479904518</isbn><isbn>1479904511</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNpFz89LwzAYxvH4C5xzd8FLLx5b3zfJm-Q9SplTGOygwm4jbROMOFvaKuhfb8GBp-fwgS88QlwhFIjAt-WyLCSgKgyRZauPxAVqywya5PZYzJA15gDSnPyDcqcTgOPcWrc9F4theAOAqWcB1EzclG3bhd6P6Stkq7797LKn4Pv6Ndt0Y9qnn0naj0txFv37EBaHnYuX--Vz-ZCvN6vH8m6dJ7Q05oa9qTTXxLLhSitirr02OkRD4BoZqwDREqInR5GZuPFBNZWPEjRGp-bi-q-bQgi7rk9733_vDnfVLy64Q6M</recordid><startdate>201306</startdate><enddate>201306</enddate><creator>Pacifico, L. D. S.</creator><creator>Ludermir, T. B.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>201306</creationdate><title>Cooperative Group Search Optimization</title><author>Pacifico, L. D. S. ; Ludermir, T. B.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-69a6b49c592d9b43599ca464ef6508d2fbe0f7511a585f9959dae3dbaf2041f83</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Animals</topic><topic>Cooperative learning</topic><topic>Evolutionary computing</topic><topic>Genetic algorithms</topic><topic>Group search optimization</topic><topic>Measurement</topic><topic>Optimization</topic><topic>Sociology</topic><topic>Statistics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Pacifico, L. D. S.</creatorcontrib><creatorcontrib>Ludermir, T. B.</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Pacifico, L. D. S.</au><au>Ludermir, T. B.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Cooperative Group Search Optimization</atitle><btitle>2013 IEEE Congress on Evolutionary Computation</btitle><stitle>CEC</stitle><date>2013-06</date><risdate>2013</risdate><spage>3299</spage><epage>3306</epage><pages>3299-3306</pages><issn>1089-778X</issn><eissn>1941-0026</eissn><isbn>1479904538</isbn><isbn>9781479904532</isbn><eisbn>147990452X</eisbn><eisbn>9781479904525</eisbn><eisbn>9781479904549</eisbn><eisbn>1479904546</eisbn><eisbn>9781479904518</eisbn><eisbn>1479904511</eisbn><abstract>Group Search Optimizer (GSO) is a population-based optimization approach inspired by animal searching behaviour and group living theory. Although competition among population members may improve their performance, greater improvements could be achieved through cooperation. In this paper, a new algorithm is presented, called Cooperative Group Search Optimizer (CGSO), based on divide-and-conquer paradigm, employing cooperative behaviour among multiple GSO groups to improve the performance of standard GSO. Nine benchmark functions are used to evaluate the performance of the proposed technique. Experimental results show that the CGSO approach is able to achieve better results than standard GSO in most of the tested problems.</abstract><pub>IEEE</pub><doi>10.1109/CEC.2013.6557974</doi><tpages>8</tpages></addata></record>
fulltext fulltext_linktorsrc
identifier ISSN: 1089-778X
ispartof 2013 IEEE Congress on Evolutionary Computation, 2013, p.3299-3306
issn 1089-778X
1941-0026
language eng
recordid cdi_ieee_primary_6557974
source IEEE Xplore All Conference Series
subjects Animals
Cooperative learning
Evolutionary computing
Genetic algorithms
Group search optimization
Measurement
Optimization
Sociology
Statistics
title Cooperative Group Search Optimization
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-14T12%3A56%3A54IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-ieee_CHZPO&rft_val_fmt=info:ofi/fmt:kev:mtx:book&rft.genre=proceeding&rft.atitle=Cooperative%20Group%20Search%20Optimization&rft.btitle=2013%20IEEE%20Congress%20on%20Evolutionary%20Computation&rft.au=Pacifico,%20L.%20D.%20S.&rft.date=2013-06&rft.spage=3299&rft.epage=3306&rft.pages=3299-3306&rft.issn=1089-778X&rft.eissn=1941-0026&rft.isbn=1479904538&rft.isbn_list=9781479904532&rft_id=info:doi/10.1109/CEC.2013.6557974&rft.eisbn=147990452X&rft.eisbn_list=9781479904525&rft.eisbn_list=9781479904549&rft.eisbn_list=1479904546&rft.eisbn_list=9781479904518&rft.eisbn_list=1479904511&rft_dat=%3Cieee_CHZPO%3E6557974%3C/ieee_CHZPO%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-i175t-69a6b49c592d9b43599ca464ef6508d2fbe0f7511a585f9959dae3dbaf2041f83%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_id=info:pmid/&rft_ieee_id=6557974&rfr_iscdi=true