增强自适应组合蚁群算法

Zelin Yao, Can Liu, Yu Wei, Xinyu Lian, Zehua Yang
{"title":"增强自适应组合蚁群算法","authors":"Zelin Yao, Can Liu, Yu Wei, Xinyu Lian, Zehua Yang","doi":"10.1109/ICARCE55724.2022.10046631","DOIUrl":null,"url":null,"abstract":"To solve the problems that ant colony algorithm (ACO) has long iterations, slow convergence, and is difficult to find the optimum, an ACO based on the annealing tempering coefficient (AHACO) is proposed, which can speed up convergence and improve the ability to find optimum. According to the distribution characteristics of path, an adaptive state transition probability (APACO) is introduced, and two types of adaptive coefficient are given. Subsequently, an adaptive evaporation coefficient is introduced to optimize convergence (AEACO). enhanced adaptive combined ACO is introduced to combine all advantages. Finally, parameters selection and simulation experiments are designed and executed. The results indicate that the effectiveness of EACACO.","PeriodicalId":416305,"journal":{"name":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhanced Adaptive Combined Ant Colony Algorithm\",\"authors\":\"Zelin Yao, Can Liu, Yu Wei, Xinyu Lian, Zehua Yang\",\"doi\":\"10.1109/ICARCE55724.2022.10046631\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To solve the problems that ant colony algorithm (ACO) has long iterations, slow convergence, and is difficult to find the optimum, an ACO based on the annealing tempering coefficient (AHACO) is proposed, which can speed up convergence and improve the ability to find optimum. According to the distribution characteristics of path, an adaptive state transition probability (APACO) is introduced, and two types of adaptive coefficient are given. Subsequently, an adaptive evaporation coefficient is introduced to optimize convergence (AEACO). enhanced adaptive combined ACO is introduced to combine all advantages. Finally, parameters selection and simulation experiments are designed and executed. The results indicate that the effectiveness of EACACO.\",\"PeriodicalId\":416305,\"journal\":{\"name\":\"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)\",\"volume\":\"71 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICARCE55724.2022.10046631\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Automation, Robotics and Computer Engineering (ICARCE)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICARCE55724.2022.10046631","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

摘要

针对蚁群算法迭代时间长、收敛速度慢、难以找到最优解的问题,提出了一种基于退火回火系数的蚁群算法(AHACO),提高了蚁群算法的收敛速度和寻优能力。根据路径的分布特点,引入了自适应状态转移概率(APACO),并给出了两种自适应系数。随后,引入自适应蒸发系数来优化收敛性(AEACO)。引入了增强型自适应组合蚁群算法,综合了上述优点。最后,设计并进行了参数选择和仿真实验。结果表明了EACACO的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Enhanced Adaptive Combined Ant Colony Algorithm
To solve the problems that ant colony algorithm (ACO) has long iterations, slow convergence, and is difficult to find the optimum, an ACO based on the annealing tempering coefficient (AHACO) is proposed, which can speed up convergence and improve the ability to find optimum. According to the distribution characteristics of path, an adaptive state transition probability (APACO) is introduced, and two types of adaptive coefficient are given. Subsequently, an adaptive evaporation coefficient is introduced to optimize convergence (AEACO). enhanced adaptive combined ACO is introduced to combine all advantages. Finally, parameters selection and simulation experiments are designed and executed. The results indicate that the effectiveness of EACACO.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Design and Implementation of MobileRobot Navigation System Based on ROS Platform Cooperative Pursuit in a Non-closed Bounded Domain 3D Reconstruction of Astronomical Site Selection Based on Multi-Source Remote Sensing Design and Implementation of Manipulator Based on Arduino Dynamic Reversible Data Hiding for Edge Contrast Enhancement of Medical Image
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1