Bio-Inspired Algorithms for Many-Objective Discrete Optimization

Bio-inspired approaches have been recently investigated as an alternative to solve intractable multi-objective problems with many objectives. In a recent paper, we proposed a many-objective algorithm named MACO/NDS which is based on ant colony optimization (ACO). Although MACO/NDS has shown to be a...

Full description

Saved in:
Bibliographic Details
Published in:Proceedings (Brazilian Conference on Intelligent Systems. Online) pp. 515 - 520
Main Authors: Martins, Luiz G.A., Franca, Tiago P., de Oliveira, Gina M.B.
Format: Conference Proceeding
Language:English
Published: IEEE 01.10.2019
Subjects:
Ant colony optimization
Approximation algorithms
discrete problems
Evolutionary algorithms
Evolutionary computation
Many objective algorithms
Materials requirements planning
Measurement
Optimization
Quality of service
Routing
ISSN:2643-6264
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Bio-inspired approaches have been recently investigated as an alternative to solve intractable multi-objective problems with many objectives. In a recent paper, we proposed a many-objective algorithm named MACO/NDS which is based on ant colony optimization (ACO). Although MACO/NDS has shown to be a competitive method against four multi-objective evolutionary algorithms (MOEAs), some open questions still remain. How would MACO/NDS behave when applied to highly complex problems where the Pareto front has a very large cardinality? Would it be well evaluated by a nondependent Pareto metric such as hypervolume? Furthermore, it should be clarified whether the good performance of MACO/NDS is due to its ACO subjacent framework or it is due to its underlying mechanisms to deal with many objectives. This last question is part of a broader investigation about the adequacy of ACOs and MOEAs to solve discrete optimization problems. Therefore, in the present paper, we use complex instances of multicast routing and multi-objective knapsack problems to compare the performance of three MOEAs (MOEA/D, NSGA-III and MEANDS) and three many-ACOs (MACO/NDS, MOACS and MOEA/D-ACO). MACO/NDS achieved good performance in both problems and it has a more balanced behaviour than the others.
AbstractList Bio-inspired approaches have been recently investigated as an alternative to solve intractable multi-objective problems with many objectives. In a recent paper, we proposed a many-objective algorithm named MACO/NDS which is based on ant colony optimization (ACO). Although MACO/NDS has shown to be a competitive method against four multi-objective evolutionary algorithms (MOEAs), some open questions still remain. How would MACO/NDS behave when applied to highly complex problems where the Pareto front has a very large cardinality? Would it be well evaluated by a nondependent Pareto metric such as hypervolume? Furthermore, it should be clarified whether the good performance of MACO/NDS is due to its ACO subjacent framework or it is due to its underlying mechanisms to deal with many objectives. This last question is part of a broader investigation about the adequacy of ACOs and MOEAs to solve discrete optimization problems. Therefore, in the present paper, we use complex instances of multicast routing and multi-objective knapsack problems to compare the performance of three MOEAs (MOEA/D, NSGA-III and MEANDS) and three many-ACOs (MACO/NDS, MOACS and MOEA/D-ACO). MACO/NDS achieved good performance in both problems and it has a more balanced behaviour than the others.
Author Franca, Tiago P.
de Oliveira, Gina M.B.
Martins, Luiz G.A.
Author_xml – sequence: 1
  givenname: Luiz G.A.
  surname: Martins
  fullname: Martins, Luiz G.A.
  email: lgamartins@ufu.br
  organization: Faculty of Computing, Federal University of Uberlandia, Uberlandia, Brazil
– sequence: 2
  givenname: Tiago P.
  surname: Franca
  fullname: Franca, Tiago P.
  email: tiagoperesfr@gmail.com
  organization: Faculty of Computing, Federal University of Uberlandia, Uberlandia, Brazil
– sequence: 3
  givenname: Gina M.B.
  surname: de Oliveira
  fullname: de Oliveira, Gina M.B.
  email: gina@ufu.br
  organization: Faculty of Computing, Federal University of Uberlandia, Uberlandia, Brazil
BookMark eNotj9lKxDAYhaMoOI59APGmL5CarWn_Ozt1K4wUXK6HJE01w3QhCcL49Bb05hz4OHxwLtHZOI0WoWtKMkoJ3G5eq7p5yxihkBFCQJ6gBIqSFqykguWcnqIVk4JjueQFSkLYLzPOCBd5vkJ3GzfhZgyz87ZLq8Pn5F38GkLaTz59UeMRt3pvTXTfNr13wXgbbdrO0Q3uR0U3jVfovFeHYJP_XqOPx4f3-hlv26emrrbYUVpGzArLBRDoOkl62jGmpegEt6qUGnLOF2CUItALDtpIzYnupTKmMEaDVIqv0c2f11lrd7N3g_LHXQmMw3LnF4TiTKo
CODEN IEEPAD
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/BRACIS.2019.00096
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Electronic Library (IEL)
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE/IET Electronic Library (IEL) (UW System Shared)
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Computer Science
EISBN 9781728142531
1728142539
EISSN 2643-6264
EndPage 520
ExternalDocumentID 8923900
Genre orig-research
GroupedDBID 6IE
6IF
6IL
6IN
AAJGR
AAWTH
ABLEC
ADZIZ
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
CHZPO
IEGSK
OCL
RIE
RIL
ID FETCH-LOGICAL-i118t-27e34909dd60f1d22b64d43ea86b953322bcaa09f439bc6b30bf6acc7ccb96aa3
IEDL.DBID RIE
IngestDate Wed Aug 27 02:42:14 EDT 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i118t-27e34909dd60f1d22b64d43ea86b953322bcaa09f439bc6b30bf6acc7ccb96aa3
PageCount 6
ParticipantIDs ieee_primary_8923900
PublicationCentury 2000
PublicationDate 2019-Oct
PublicationDateYYYYMMDD 2019-10-01
PublicationDate_xml – month: 10
  year: 2019
  text: 2019-Oct
PublicationDecade 2010
PublicationTitle Proceedings (Brazilian Conference on Intelligent Systems. Online)
PublicationTitleAbbrev BRACIS
PublicationYear 2019
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0003203455
Score 1.7077898
Snippet Bio-inspired approaches have been recently investigated as an alternative to solve intractable multi-objective problems with many objectives. In a recent...
SourceID ieee
SourceType Publisher
StartPage 515
SubjectTerms Ant colony optimization
Approximation algorithms
discrete problems
Evolutionary algorithms
Evolutionary computation
Many objective algorithms
Materials requirements planning
Measurement
Optimization
Quality of service
Routing
Title Bio-Inspired Algorithms for Many-Objective Discrete Optimization
URI https://ieeexplore.ieee.org/document/8923900
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1JTwIxFG6QePCECsY9PXi00pmWTnsTUCIHgbgk3EhXHSMzhsXfbzszwsWLt6aXJq99r9_3VgCuSCI6livuHy81iBqqkZSSIqe1pUQnxBlTDJtIRiM-nYpJDVxvamGstUXymb0JyyKWb3K9Dq6yNvdoRGBP0HeShJW1Wht_CokxoZ1OFbiMsGj3nrr94XPI3gotKXHRln87QKX4PwaN_528D1rbQjw42XwxB6Bms0PQ-J3EACvFbILbXpqjYRai5tbA7udb7jn_-3wJPSSFj17f0Vh9lKYN3qXeUnioDMfeWsyrMswWeB3cv_QfUDUbAaWeEqxQnFhCBRbGMOwiE8eKeUETKzlTIWPUb2gpsXAecCjNFMHKMal1orUSTEpyBOpZntljAB3jHrcZz5NjR5mLlGcYghPOZESjyPET0AwCmX2V7S9mlSxO_94-A3tB4mW-2zmorxZrewF29fcqXS4uizv7AaEdmQo
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1JTwIxGG0ImugJFYy7PXh0pDMtnfYmoAQiWxQTbqSrjhHGsPj7bYcRLl68Nb00-dp-fa_f8gC4wTGvGSaZO7xEB0QTFQghSGCVMgSrGFutM7GJuN9n4zEfFsDtphbGGJMln5k7P8xi-TpVK_9VVmUOjXDkCPqOV87Kq7U2Pyo4QpjUannoMkS82niuNzsvPn_LN6VEWWP-rYRK9oK0Sv9b-wBUtqV4cLh5ZA5BwcyOQOlXiwHmV7MM7htJGnRmPm5uNKx_vqWO9b9PF9CBUthzNz4YyI-1c4MPifMVDizDgfMX07wQswJeW4-jZjvI1RGCxJGCZRDFBhOOuNYU2VBHkaTO1NgIRqXPGXUTSgjErYMcUlGJkbRUKBUrJTkVAh-D4iydmRMALWUOuWnHlCNLqA2l4xicYUZFSMLQslNQ9gaZfK0bYExyW5z9PX0N9tqjXnfS7fSfzsG-t_46--0CFJfzlbkEu-p7mSzmV9n-_QDLwpxT
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=Proceedings+%28Brazilian+Conference+on+Intelligent+Systems.+Online%29&rft.atitle=Bio-Inspired+Algorithms+for+Many-Objective+Discrete+Optimization&rft.au=Martins%2C+Luiz+G.A.&rft.au=Franca%2C+Tiago+P.&rft.au=de+Oliveira%2C+Gina+M.B.&rft.date=2019-10-01&rft.pub=IEEE&rft.eissn=2643-6264&rft.spage=515&rft.epage=520&rft_id=info:doi/10.1109%2FBRACIS.2019.00096&rft.externalDocID=8923900