Multi-area Path Planning for Wireless Sensor Networks Based on Double Populations Ant Colony Optimization Algorithm

Aiming at the problem that the ant colony algorithm(ACO) is slow to converge and easily fall into the local optimal value in the path planning of multi-area wireless sensor networks(WSNs), an improved ant colony optimization algorithm is proposed. First, a grid method was used to model two different...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics) s. 152 - 159
Hlavní autoři: Zhai, Chenxuan, Wang, Minghua, Jiang, Kaiwu, Wang, Yan, Fan, Bo, Wang, Chao
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.11.2020
Témata:
Ant Colony Optimization algorithms(ACO)
Convergence
Double Populations Ant Colony Optimization algorithms(DPACO)
Electrical engineering
Heuristic algorithms
Path planning
Sociology
Statistics
Wireless sensor networks
Wireless Sensor Networks(WSNs)
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Abstract Aiming at the problem that the ant colony algorithm(ACO) is slow to converge and easily fall into the local optimal value in the path planning of multi-area wireless sensor networks(WSNs), an improved ant colony optimization algorithm is proposed. First, a grid method was used to model two different obstacle environments. Second, to improve the efficiency of ant search in the early stages set a non-uniform initial pheromone concentration, and introduce new populations to expand the search space of the algorithm and avoid the algorithm falling into a local optimum. Finally, it is proposed that the elite ant pheromone update principle and adaptively adjusts the volatile coefficients to ensure the global search capability and improve the convergence speed of the algorithm. The experimental results show that the algorithm has a high global search capability and significantly faster convergence speed, which verifies the effectiveness and superiority of the algorithm.
AbstractList Aiming at the problem that the ant colony algorithm(ACO) is slow to converge and easily fall into the local optimal value in the path planning of multi-area wireless sensor networks(WSNs), an improved ant colony optimization algorithm is proposed. First, a grid method was used to model two different obstacle environments. Second, to improve the efficiency of ant search in the early stages set a non-uniform initial pheromone concentration, and introduce new populations to expand the search space of the algorithm and avoid the algorithm falling into a local optimum. Finally, it is proposed that the elite ant pheromone update principle and adaptively adjusts the volatile coefficients to ensure the global search capability and improve the convergence speed of the algorithm. The experimental results show that the algorithm has a high global search capability and significantly faster convergence speed, which verifies the effectiveness and superiority of the algorithm.
Author Fan, Bo
Wang, Minghua
Wang, Chao
Jiang, Kaiwu
Zhai, Chenxuan
Wang, Yan
Author_xml – sequence: 1
  givenname: Chenxuan
  surname: Zhai
  fullname: Zhai, Chenxuan
  email: 18119357286@163.com
  organization: School of Electrical Engineering, University of South China,Hengyang,China
– sequence: 2
  givenname: Minghua
  surname: Wang
  fullname: Wang, Minghua
  email: wmh1013@126.com
  organization: School of Electrical Engineering, University of South China,Hengyang,China
– sequence: 3
  givenname: Kaiwu
  surname: Jiang
  fullname: Jiang, Kaiwu
  email: jkw8845@163.com
  organization: School of Electrical Engineering, University of South China,Hengyang,China
– sequence: 4
  givenname: Yan
  surname: Wang
  fullname: Wang, Yan
  email: wangyan5406@163.com
  organization: School of Electrical Engineering, University of South China,Hengyang,China
– sequence: 5
  givenname: Bo
  surname: Fan
  fullname: Fan, Bo
  email: fanbohysd@163.com
  organization: School of Electrical Engineering, University of South China,Hengyang,China
– sequence: 6
  givenname: Chao
  surname: Wang
  fullname: Wang, Chao
  email: wchao@163.com
  organization: School of Electrical Engineering, University of South China,Hengyang,China
BookMark eNotUM1OAjEYrIkeFHkCL32Bxf7utkdcEE1QSMB4JGX3Axq7LWlLDD69689hMplMMpOZG3TpgweEppSMKCX63q4P1u9TMYsAvg5dUS9XP7TqTMwTk01Rn7cQO5NtkyThSo8YYWRECBH0Ag11pWjFepSiktcovZxctoWJYPDS5ANeOuN934B3IeJ3G8FBSngFPvX6FfJniB8JP5gELQ4eT8Jp6wAvw_Hk-srgEx77jOvggj_jxTHbzn79Gnjs9iHafOhu0dXOuATDfx6gt8fpun4q5ovZcz2eF5ZSlQvZkpJtG6kE19A2VGohBFRNWSrYSd1qxtqqbBtBgVWgmNT9KK7ahquGC8L4AN395VoA2Byj7S86bzTTVHLFvwFP8Wji
CODEN IEEPAD
ContentType Conference Proceeding
DBID 6IE
6IL
CBEJK
RIE
RIL
DOI 10.1109/iThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00041
DatabaseName IEEE Electronic Library (IEL) Conference Proceedings
IEEE Xplore POP ALL
IEEE Xplore All Conference Proceedings
IEEE Xplore
IEEE Proceedings Order Plans (POP All) 1998-Present
DatabaseTitleList
Database_xml – sequence: 1
  dbid: RIE
  name: IEEE Xplore
  url: https://ieeexplore.ieee.org/
  sourceTypes: Publisher
DeliveryMethod fulltext_linktorsrc
Discipline Statistics
EISBN 9781728176475
1728176476
EndPage 159
ExternalDocumentID 9291538
Genre orig-research
GrantInformation_xml – fundername: Hunan Provincial Innovation Foundation for Postgraduate
  grantid: CX20200933
  funderid: 10.13039/501100010083
– fundername: National Natural Science Foundation of China
  grantid: 61971215,61871209
  funderid: 10.13039/501100001809
– fundername: University of South China
  grantid: 2019KFZ12
  funderid: 10.13039/501100009020
GroupedDBID 6IE
6IL
CBEJK
RIE
RIL
ID FETCH-LOGICAL-i118t-5d062bc58439edc159444e7c668ef59d922d76dc41e27e825917638dc38c34023
IEDL.DBID RIE
IngestDate Thu Jun 29 18:38:12 EDT 2023
IsPeerReviewed false
IsScholarly false
Language English
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-i118t-5d062bc58439edc159444e7c668ef59d922d76dc41e27e825917638dc38c34023
PageCount 8
ParticipantIDs ieee_primary_9291538
PublicationCentury 2000
PublicationDate 2020-Nov.
PublicationDateYYYYMMDD 2020-11-01
PublicationDate_xml – month: 11
  year: 2020
  text: 2020-Nov.
PublicationDecade 2020
PublicationTitle 2020 International Conferences on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) and IEEE Congress on Cybermatics (Cybermatics)
PublicationTitleAbbrev ITHINGS-GREENCOM-CPSCOM-SMARTDATA-CYBERMATICS
PublicationYear 2020
Publisher IEEE
Publisher_xml – name: IEEE
Score 1.746153
Snippet Aiming at the problem that the ant colony algorithm(ACO) is slow to converge and easily fall into the local optimal value in the path planning of multi-area...
SourceID ieee
SourceType Publisher
StartPage 152
SubjectTerms Ant Colony Optimization algorithms(ACO)
Convergence
Double Populations Ant Colony Optimization algorithms(DPACO)
Electrical engineering
Heuristic algorithms
Path planning
Sociology
Statistics
Wireless sensor networks
Wireless Sensor Networks(WSNs)
Title Multi-area Path Planning for Wireless Sensor Networks Based on Double Populations Ant Colony Optimization Algorithm
URI https://ieeexplore.ieee.org/document/9291538
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LS8NAEB7aIuLJRyu-2YNH1-a5SY41tniQGqhKbyX7iBbaRJpU6L93dhMrghdPeUBImGF2vi873wzAdeaJyOLKpkJYPvV8wShHVEE5t20W-mnmmz7br4_BeBxOp1HSgputFkYpZYrP1K0-NXv5shBr_ausj6lcB2gb2kEQ1FqtXRg2bTP783rSJTUFKxhNNE4m-jBZohPu0yql8YYrAwRFaRrLIT10LNOv0_41XMXkltH-_77qAHo_Ij2SbNPPIbRUfgR7GjzWvZe7UBp1LU0RF5IEkR75nlBEEKkSXfe6wHWOTJDJ4vW4LggvyR0mNkmKnCC45gt8yXbGV0kGeUViXC_zDXnCxWbZqDjJYPFWrObV-7IHL6Phc_xAmyELdI7coqK-tJjDBeIQN1JSILrxPE8FgrFQZX4kI8eRAZPCs5UTKOSTyO8wZqVwQ-Ei-XSPoZMXuToBwlhqc98RlsUzD-EwPsoyl4VZZglkJuIUutp6s4-6j8asMdzZ37fPYU-7p9b9XUCnWq3VJeyIT7Ti6so4_wsTmbTh
linkProvider IEEE
linkToHtml http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwlV1LT8JAEJ4gGuXkA4xv9-DRlT52l_aIVYIRKwlouJHudqskUAwUE_690y1iTLx46iNp2sx2Zr6vnW8G4CphyrektqlSFqeMK0ElogoqpW0Lj0cJN322XzuNMPQGA79bguu1FkZrbYrP9E2-a_7lx1O1yD-V1TGV5w66AZucMccu1FrbcL9qnFkfFbMuqSlZQX-iQbeXb3oTXIa7KItosJTaQEE1N63lkCA6lunYaf8ar2KyS2v3f8-1B7UfmR7prhPQPpR0egCVHD4W3ZerMDf6WhohMiRdxHrke0YRQaxK8srXMUY60kMui8dhURI-J7eY2mIyTQnCaznGm6ynfM1JM81IgBEzXZJnDDeTlY6TNMdv09koe5_U4KV13w_adDVmgY6QXWSUx5ZwpEIk4vo6VohvGGO6oYTwdML92HecuCFixWztNDQySmR46LWxcj3lIv10D6GcTlN9BESIyJbcUZYlE4aAGC8ViSu8JLEUchN1DNXcesOPopPGcGW4k79PX8JOu__UGXYewsdTqORLVagAz6CczRb6HLbUJ1p0dmFehC-iEbgo
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=2020+International+Conferences+on+Internet+of+Things+%28iThings%29+and+IEEE+Green+Computing+and+Communications+%28GreenCom%29+and+IEEE+Cyber%2C+Physical+and+Social+Computing+%28CPSCom%29+and+IEEE+Smart+Data+%28SmartData%29+and+IEEE+Congress+on+Cybermatics+%28Cybermatics%29&rft.atitle=Multi-area+Path+Planning+for+Wireless+Sensor+Networks+Based+on+Double+Populations+Ant+Colony+Optimization+Algorithm&rft.au=Zhai%2C+Chenxuan&rft.au=Wang%2C+Minghua&rft.au=Jiang%2C+Kaiwu&rft.au=Wang%2C+Yan&rft.date=2020-11-01&rft.pub=IEEE&rft.spage=152&rft.epage=159&rft_id=info:doi/10.1109%2FiThings-GreenCom-CPSCom-SmartData-Cybermatics50389.2020.00041&rft.externalDocID=9291538