融合多策略改进的自适应蚁群算法

TP181; 针对蚁群算法(ant colony algorithm,ACO)收敛速度慢和易陷入局部最优等问题,提出一种融合多策略改进的自适应蚁群算法(improved ant colony algorithm,IACO).首先,通过透镜成像反向学习的拉丁超立方策略进行种群初始化,增加种群多样性的同时也提升初始解的质量;其次,通过动态信息素更新规则和自适应调整机制对信息素进行更新,进一步提高全局搜索能力;再次,采用t分布对信息素浓度进行变异扰动,可以增强其跳出局部最优能力;最后,将IACO与标准ACO及其他经典群智能优化算法在多个标准测试函数上作仿真对比,在Rastrigin、Sphere和...

Full description

Saved in:
Bibliographic Details
Published in南通大学学报（自然科学版） Vol. 23; no. 4; pp. 36 - 44
Main Authors 刘禹彤, 李媛, 郑新宇
Format Journal Article
LanguageChinese
Published 沈阳工业大学 理学院,辽宁 沈阳 110870 2024
Subjects
反向学习
蚁群算法
Latin hypercube sampling
ant colony algorithm
自适应蚁群算法
adaptive ant colony algorithm
t-distri-bution
reverse learning
拉丁超立方抽样
t分布
Online AccessGet full text
ISSN1673-2340
DOI10.12194/j.ntu.20240508001

Cover

Abstract TP181; 针对蚁群算法(ant colony algorithm,ACO)收敛速度慢和易陷入局部最优等问题,提出一种融合多策略改进的自适应蚁群算法(improved ant colony algorithm,IACO).首先,通过透镜成像反向学习的拉丁超立方策略进行种群初始化,增加种群多样性的同时也提升初始解的质量;其次,通过动态信息素更新规则和自适应调整机制对信息素进行更新,进一步提高全局搜索能力;再次,采用t分布对信息素浓度进行变异扰动,可以增强其跳出局部最优能力;最后,将IACO与标准ACO及其他经典群智能优化算法在多个标准测试函数上作仿真对比,在Rastrigin、Sphere和 Ackley 3 个测试函数上分别与标准 ACO、GA(genetic algorithm)和 PSO(particle swarm optimization)进行对比,同时,在Rastrigin上分别控制蚂蚁数量和启发式信息权重与标准ACO进行对比,实验结果表明IACO具有更好的收敛性能和稳定性能.
AbstractList TP181; 针对蚁群算法(ant colony algorithm,ACO)收敛速度慢和易陷入局部最优等问题,提出一种融合多策略改进的自适应蚁群算法(improved ant colony algorithm,IACO).首先,通过透镜成像反向学习的拉丁超立方策略进行种群初始化,增加种群多样性的同时也提升初始解的质量;其次,通过动态信息素更新规则和自适应调整机制对信息素进行更新,进一步提高全局搜索能力;再次,采用t分布对信息素浓度进行变异扰动,可以增强其跳出局部最优能力;最后,将IACO与标准ACO及其他经典群智能优化算法在多个标准测试函数上作仿真对比,在Rastrigin、Sphere和 Ackley 3 个测试函数上分别与标准 ACO、GA(genetic algorithm)和 PSO(particle swarm optimization)进行对比,同时,在Rastrigin上分别控制蚂蚁数量和启发式信息权重与标准ACO进行对比,实验结果表明IACO具有更好的收敛性能和稳定性能.
Abstract_FL To address the problems of slow convergence and local optima entrapment in the ant colony algorithm(ACO),this paper proposes an improved ant colony algorithm(IACO)integrating multiple strategies.First,a Latin hy-percube strategy based on lens imaging inverse learning is employed for population initialization,which enhances popu-lation diversity while improving the quality of initial solutions.Second,dynamic pheromone update rules and adaptive adjustment mechanisms are implemented for pheromone updates,further enhancing global search capability.Third,distribution-based mutation perturbation of pheromone concentration is adopted to strengthen the ability to escape lo-cal optima.Finally,comparative simulation experiments are conducted between IACO and the standard ACO as well as other classic swarm intelligence optimization algorithms on multiple standard test functions.Specifically,compa-risons are made with standard ACO,GA(genetic algorithm),and PSO(particle swarm optimization)on three test functions:Rastrigin,Sphere,and Ackley.Additionally,on the Rastrigin function,comparisons are made with the stan-dard ACO by controlling ant population size and heuristic information weight.The experimental results demonstrate that IACO exhibits superior convergence performance and stability.
Author 刘禹彤
李媛
郑新宇
AuthorAffiliation 沈阳工业大学 理学院,辽宁 沈阳 110870
AuthorAffiliation_xml – name: 沈阳工业大学 理学院,辽宁 沈阳 110870
Author_FL ZHENG Xinyu
LI Yuan
LIU Yutong
Author_FL_xml – sequence: 1
  fullname: LIU Yutong
– sequence: 2
  fullname: LI Yuan
– sequence: 3
  fullname: ZHENG Xinyu
Author_xml – sequence: 1
  fullname: 刘禹彤
– sequence: 2
  fullname: 李媛
– sequence: 3
  fullname: 郑新宇
BookMark eNrjYmDJy89LZWCQMjTQMzQytDTRz9LLKynVMzIwMjEwNbAwMDBkYeA0NDM31jUyNjHgYOAtLs5MAooaGlkaGVhyMui-mNf7dELH0yWznq-d9nzq0mdTdr7YP_v5rJYX7ateNjQ93TXlxazG5_uWPF83_dnmqTwMrGmJOcWpvFCam0HDzTXE2UO3PDEvLTEvPT4rv7QoDygTn1eSXlFZkQR2h4mBgYkxCUoBWsNNPw
ClassificationCodes TP181
ContentType Journal Article
Copyright Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
Copyright_xml – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved.
DBID 2B.
4A8
92I
93N
PSX
TCJ
DOI 10.12194/j.ntu.20240508001
DatabaseName Wanfang Data Journals - Hong Kong
WANFANG Data Centre
Wanfang Data Journals
万方数据期刊 - 香港版
China Online Journals (COJ)
China Online Journals (COJ)
DatabaseTitleList
DeliveryMethod fulltext_linktorsrc
DocumentTitle_FL Improved adaptive ant colony algorithm by incorporating multiple strategies
EndPage 44
ExternalDocumentID ntgxyxb202404004
GrantInformation_xml – fundername: 辽宁省兴辽英才计划项目
  funderid: (XLYC2008005)
GroupedDBID 2B.
4A8
92I
93N
ALMA_UNASSIGNED_HOLDINGS
GROUPED_DOAJ
PSX
TCJ
ID FETCH-wanfang_journals_ntgxyxb2024040043
ISSN 1673-2340
IngestDate Thu May 29 03:54:36 EDT 2025
IsPeerReviewed false
IsScholarly true
Issue 4
Keywords 反向学习
蚁群算法
Latin hypercube sampling
ant colony algorithm
自适应蚁群算法
adaptive ant colony algorithm
t-distri-bution
reverse learning
拉丁超立方抽样
t分布
Language Chinese
LinkModel OpenURL
MergedId FETCHMERGED-wanfang_journals_ntgxyxb2024040043
ParticipantIDs wanfang_journals_ntgxyxb202404004
PublicationCentury 2000
PublicationDate 2024
PublicationDateYYYYMMDD 2024-01-01
PublicationDate_xml – year: 2024
  text: 2024
PublicationDecade 2020
PublicationTitle 南通大学学报（自然科学版）
PublicationTitle_FL Journal of Nantong University(Natural Science Edition)
PublicationYear 2024
Publisher 沈阳工业大学 理学院,辽宁 沈阳 110870
Publisher_xml – name: 沈阳工业大学 理学院,辽宁 沈阳 110870
SSID ssib001129209
ssib006703813
ssj0002912245
ssib006563819
ssib036435926
ssib013937516
ssib051370593
ssib002039883
ssib000969275
Score 4.67933
Snippet TP181; 针对蚁群算法(ant colony algorithm,ACO)收敛速度慢和易陷入局部最优等问题,提出一种融合多策略改进的自适应蚁群算法(improved ant colony...
SourceID wanfang
SourceType Aggregation Database
StartPage 36
Title 融合多策略改进的自适应蚁群算法
URI https://d.wanfangdata.com.cn/periodical/ntgxyxb202404004
Volume 23
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR1NaxNBdKj14kUUFb-p4AMhbN2PmZ2Z426zoQh6qtBbyaabeoqgKdSeVAS9CB5ahQie6kXQiyc96K9JE_-F773d7A6mYPWyTGbe98vw3szOmxXipuwZ1bVd60UyNx7OROPlmwEddvBl0TNFt-AKubv34tX78s66Wl840XdOLW0P8-Xe7pF1Jf_jVexDv1KV7D94tiaKHdhG_-ITPYzPY_kYMgM2A9OGTIH1wRhqJBJsApmGpA02pobFTgVZDFZCagkr7YBNeSgBI6nHIDxiWTr6YEKikyYETywQJiDgNCPiRDkDq4lgGkH59cpZgkuIKA-NMimShEVKNDfakMROIwaTsGwdSFdY_loSTYKlLD_i2sDBwiHLwCVWvZfBzFFew0gx64p6IJJsQNAKbTAZk0MN02YEaUbMKCa7pT6DoHm1uzUSNpuibICQBbHEFI1B7DQrhLKbI7VvsdnRyK4dEN9CEuIfnr2TkdAV96A1z6dFZRXlp1CqiBLryAuj8k6qWcgpS6yrqSWd-BHFTiZSXow5F-MwxkgOcpiULJPavqK0P2gien3OcjDc2nmykzOQ5GtzT4ZaB8rZeqiWsdZ9e005eOhc6xb6kTVOGoprAFroN781vW-uxwO6ZFE1y44Is15lmzRfBZH2Z9fsUcYUWnq_S6eNa2tV9W2k6-05TbnAbtDvDracXHDtjDhdLeKWknJGnhULuw_OCW_64fX4zavxwWjy-e1k_-Ph3rfpz_eT0Yvpy0-_nj4ff9-bjp5NfhxMvrw7_Lp_XtzqZGsrq17FYqOa8I83_rRmdEEsDh4OiotiSReb_ULRFwIUCt_zLdoIF1ZBP1dh3rXykrjxV3KXjwFzRZyidrkHeFUsDh9tF9cwKx7m19mlvwFDJoEF
linkProvider Directory of Open Access Journals
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%3Ajournal&rft.genre=article&rft.atitle=%E8%9E%8D%E5%90%88%E5%A4%9A%E7%AD%96%E7%95%A5%E6%94%B9%E8%BF%9B%E7%9A%84%E8%87%AA%E9%80%82%E5%BA%94%E8%9A%81%E7%BE%A4%E7%AE%97%E6%B3%95&rft.jtitle=%E5%8D%97%E9%80%9A%E5%A4%A7%E5%AD%A6%E5%AD%A6%E6%8A%A5%EF%BC%88%E8%87%AA%E7%84%B6%E7%A7%91%E5%AD%A6%E7%89%88%EF%BC%89&rft.au=%E5%88%98%E7%A6%B9%E5%BD%A4&rft.au=%E6%9D%8E%E5%AA%9B&rft.au=%E9%83%91%E6%96%B0%E5%AE%87&rft.date=2024&rft.pub=%E6%B2%88%E9%98%B3%E5%B7%A5%E4%B8%9A%E5%A4%A7%E5%AD%A6+%E7%90%86%E5%AD%A6%E9%99%A2%2C%E8%BE%BD%E5%AE%81+%E6%B2%88%E9%98%B3+110870&rft.issn=1673-2340&rft.volume=23&rft.issue=4&rft.spage=36&rft.epage=44&rft_id=info:doi/10.12194%2Fj.ntu.20240508001&rft.externalDocID=ntgxyxb202404004
thumbnail_s http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fntgxyxb%2Fntgxyxb.jpg