Dynamic multiobjective optimization of war resource allocation using adaptive genetic algorithms

Genetic algorithms (GA) are often well suited for multiobjective optimization problems. The major objective of this research is to optimize the war resource allocations of sorties, for a given war scenario, using genetic algorithms. The war is simulated using THUNDER software. THUNDER software is a...

Full description

Saved in:
Bibliographic Details
Published in:2001 IEEE Southeastcon pp. 160 - 165
Main Authors: Palaniappan, S., Zein-Sabatto, S., Sekmen, A.
Format: Conference Proceeding
Language:English
Published: IEEE 2001
Subjects:
Analytical models
Convergence
Fuzzy logic
Genetic algorithms
Genetic mutations
Intelligent systems
Military computing
Resource management
Stochastic resonance
Testing
ISBN:0780367480, 9780780367487
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Abstract Genetic algorithms (GA) are often well suited for multiobjective optimization problems. The major objective of this research is to optimize the war resource allocations of sorties, for a given war scenario, using genetic algorithms. The war is simulated using THUNDER software. THUNDER software is a stochastic, two-sided, analytical simulation of campaign-level military operations. The simulation is subject to internal unknown noises similar to real war cases. Due to these noises and discreteness in the simulation, as well as in real wars, an adaptive GA approach has been applied to solve this multiobjective optimization problem. Transforming this multiobjective optimization problem to a form suitable for direct implementation of GA was a major accomplishment of this research. A suitable fitness function was chosen after careful research and testing on the GA. Furthermore, the GA parameters were adaptively set to yield smoother and faster fitness convergence. Two fuzzy logic mechanisms were used to adapt the GA parameters. In the first mechanism, the mutation and crossover rates were changed adaptively. In the second mechanism, the fitness function coefficients are changed dynamically in each run. Testing results showed that the adaptive GA outperforms the conventional GA search in this multiobjective optimization problem and was effectively able to allocate forces for war scenarios.
AbstractList Genetic algorithms (GA) are often well suited for multiobjective optimization problems. The major objective of this research is to optimize the war resource allocations of sorties, for a given war scenario, using genetic algorithms. The war is simulated using THUNDER software. THUNDER software is a stochastic, two-sided, analytical simulation of campaign-level military operations. The simulation is subject to internal unknown noises similar to real war cases. Due to these noises and discreteness in the simulation, as well as in real wars, an adaptive GA approach has been applied to solve this multiobjective optimization problem. Transforming this multiobjective optimization problem to a form suitable for direct implementation of GA was a major accomplishment of this research. A suitable fitness function was chosen after careful research and testing on the GA. Furthermore, the GA parameters were adaptively set to yield smoother and faster fitness convergence. Two fuzzy logic mechanisms were used to adapt the GA parameters. In the first mechanism, the mutation and crossover rates were changed adaptively. In the second mechanism, the fitness function coefficients are changed dynamically in each run. Testing results showed that the adaptive GA outperforms the conventional GA search in this multiobjective optimization problem and was effectively able to allocate forces for war scenarios.
Author Palaniappan, S.
Sekmen, A.
Zein-Sabatto, S.
Author_xml – sequence: 1
  givenname: S.
  surname: Palaniappan
  fullname: Palaniappan, S.
  organization: Tennessee State Univ., Nashville, TN, USA
– sequence: 2
  givenname: S.
  surname: Zein-Sabatto
  fullname: Zein-Sabatto, S.
– sequence: 3
  givenname: A.
  surname: Sekmen
  fullname: Sekmen, A.
BookMark eNotkMFOhDAYhJuoie66D6CnvgDY0tLSo8F1Ndm4B_W8_sBf7AbohoJmfXqJOJdJZjLfYRbkvPMdEnLDWcw5M3ev63z3EieM8dgkgjN9RhZMZ0woLTN2SVYhHNgkmUqRmSvy8XDqoHUlbcdmcL44YDm4L6T-OLjW_cCUddRb-g097TH4sS-RQtP4cq7G4LqaQgXHv1mNHQ4TDZra9274bMM1ubDQBFz9-5K8P67f8qdou9s85_fbyHGdDFEqM2HQKCYLKHQmK1VZVVhAk5nSVloqKHXBmRVWy0pipVVqrU0MpAVDhWJJbmeuQ8T9sXct9Kf9_IH4BU4JV6w
ContentType Conference Proceeding
DBID 6IE
6IH
CBEJK
RIE
RIO
DOI 10.1109/SECON.2001.923107
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Proceedings Order Plan (POP) 1998-present by volume
IEEE Xplore All Conference Proceedings
IEEE/IET Electronic Library (IEL) (UW System Shared)
IEEE Proceedings Order Plans (POP) 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
EndPage 165
ExternalDocumentID 923107
GroupedDBID 6IE
6IH
6IK
6IL
AAJGR
AAVQY
AAWTH
ALMA_UNASSIGNED_HOLDINGS
BEFXN
BFFAM
BGNUA
BKEBE
BPEOZ
CBEJK
OCL
RIE
RIL
RIO
ID FETCH-LOGICAL-i172t-54839e9604bab784d6df6bfae989cfd746ac7b10f3f74d4ed765fff29a5b0e6e3
IEDL.DBID RIE
ISBN 0780367480
9780780367487
ISICitedReferencesCount 27
ISICitedReferencesURI http://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=Summon&SrcAuth=ProQuest&DestLinkType=CitingArticles&DestApp=WOS_CPL&KeyUT=000169430300030&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
IngestDate Tue Aug 26 18:58:05 EDT 2025
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i172t-54839e9604bab784d6df6bfae989cfd746ac7b10f3f74d4ed765fff29a5b0e6e3
PageCount 6
ParticipantIDs ieee_primary_923107
PublicationCentury 2000
PublicationDate 20010000
PublicationDateYYYYMMDD 2001-01-01
PublicationDate_xml – year: 2001
  text: 20010000
PublicationDecade 2000
PublicationTitle 2001 IEEE Southeastcon
PublicationTitleAbbrev SECON
PublicationYear 2001
Publisher IEEE
Publisher_xml – name: IEEE
SSID ssj0000454389
Score 1.3931899
Snippet Genetic algorithms (GA) are often well suited for multiobjective optimization problems. The major objective of this research is to optimize the war resource...
SourceID ieee
SourceType Publisher
StartPage 160
SubjectTerms Analytical models
Convergence
Fuzzy logic
Genetic algorithms
Genetic mutations
Intelligent systems
Military computing
Resource management
Stochastic resonance
Testing
Title Dynamic multiobjective optimization of war resource allocation using adaptive genetic algorithms
URI https://ieeexplore.ieee.org/document/923107
WOSCitedRecordID wos000169430300030&url=https%3A%2F%2Fcvtisr.summon.serialssolutions.com%2F%23%21%2Fsearch%3Fho%3Df%26include.ft.matches%3Dt%26l%3Dnull%26q%3D
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1JS8NAFB5s8eDJreLOHLymnZhkXuasFk-loEJv9c1WK7QpaVr_vrOEiuBFmEMSJmGYDG__vkfInQVmUViWlEqYxPlfWYLAWJJKLABSLATD0GwCRqNyMhHjlmc7YGGMMaH4zPT9Zcjl60ptfKhsEIwR6JAOAI9QrV04xTPJOd0bHPPSSWXIy5bRaXcPbVIzZWLw4tsEeu8w7ceP_mquEnTL8PBfqzoivR-MHh3vtM8x2TPLU_L-GBvM01AnWMnPKM5o5QTDokVc0srSL6xp3QbuqU-9x8Ad9VXwM4oaV-E1d7g8xtHNmFX1vPlYrHvkbfj0-vCctD0UkrkzTZrEOSSZMJ6BRaKEMtdcWy4tGlEKZTXkHBXIlNnMQq5zo4EX1tp7gYVkhpvsjHSX1dKcE1oIRGWLDA0rc-TcDemsS0SbMsWwuCAnfnOmq0iTMY37cvnn0ytyEIu5_Lgm3abemBuyr7bNfF3fhl_7DcX6pJ8
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV3LS8MwGA86BT35mvg2B6_d0jWP5qwOxTkGTthtfmmTOWHr6Dr9982jTAQvQg5tSUtIw_f-_T6EbowgBqQhUZpJHVn_K4lAEBLFCpgQMTBJwDebEP1-OhrJQc2z7bEwWmtffKZb7tLn8vMiW7lQWdsbI2ITbTFKOySAtdYBFcclZ7Wvd81TK5cFTWtOp_W9qNOaMZHtF9co0PmHcSt89ld7Fa9dunv_Wtc-av6g9PBgrX8O0IaeH6G3u9BiHvtKwUJ9BIGGCysaZjXmEhcGf0GJyzp0j13yPYTusKuDn2DIYeFfs8fLoRztjElRTqv32bKJXrv3w9uHqO6iEE2tcVJF1iVJpHYcLAqUSGnOc8OVAS1TmZlcUA6ZUDExiRE0pzoXnBljOhKYIprr5Bg15sVcnyDMJEBmWAKapBQ4t0NZ-xLAxCQjwE7Roduc8SIQZYzDvpz9-fQa7TwMn3vj3mP_6RzthtIuNy5QoypX-hJtZ5_VdFle-d_8DYRQp-Y
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=2001+IEEE+Southeastcon&rft.atitle=Dynamic+multiobjective+optimization+of+war+resource+allocation+using+adaptive+genetic+algorithms&rft.au=Palaniappan%2C+S.&rft.au=Zein-Sabatto%2C+S.&rft.au=Sekmen%2C+A.&rft.date=2001-01-01&rft.pub=IEEE&rft.isbn=9780780367487&rft.spage=160&rft.epage=165&rft_id=info:doi/10.1109%2FSECON.2001.923107&rft.externalDocID=923107
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9780780367487/lc.gif&client=summon&freeimage=true
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9780780367487/mc.gif&client=summon&freeimage=true
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=9780780367487/sc.gif&client=summon&freeimage=true