Use of Ant Colony Optimization Algorithm for Determining Traveling Salesman Problem Routes

B. P. Silalahi, Nur Fathiah, Prapto Tri Supriyo
{"title":"Use of Ant Colony Optimization Algorithm for Determining Traveling Salesman Problem Routes","authors":"B. P. Silalahi, Nur Fathiah, Prapto Tri Supriyo","doi":"10.15642/mantik.2019.5.2.100-111","DOIUrl":null,"url":null,"abstract":"Ant Colony Optimization is one of the meta-heuristic methods used to solve combinatorial optimization problems that are quite difficult. Ant Colony Optimization algorithm is inspired by ant behavior in the real world to build the shortest path between food sources and their nests. Traveling Salesman Problem is a problem in optimization. Traveling Salesman Problem is a problem to find the minimum distance from the initial node to the whole node with each node must be visited exactly once and must return to the initial node. Traveling Salesman Problem is a non-deterministic polynomial-time complete problem. This research discusses the solution of the Traveling Salesman Problem using the Ant Colony Optimization algorithm and also using the exact algorithm. The results showed that the greater the size of the Traveling Salesman Problem case, the longer the execution time required. The results also showed that the execution times of the Ant Colony Optimization are much faster than the execution time of the exact method.","PeriodicalId":32704,"journal":{"name":"Mantik Jurnal Matematika","volume":"82 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2019-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"9","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mantik Jurnal Matematika","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15642/mantik.2019.5.2.100-111","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

Abstract

Ant Colony Optimization is one of the meta-heuristic methods used to solve combinatorial optimization problems that are quite difficult. Ant Colony Optimization algorithm is inspired by ant behavior in the real world to build the shortest path between food sources and their nests. Traveling Salesman Problem is a problem in optimization. Traveling Salesman Problem is a problem to find the minimum distance from the initial node to the whole node with each node must be visited exactly once and must return to the initial node. Traveling Salesman Problem is a non-deterministic polynomial-time complete problem. This research discusses the solution of the Traveling Salesman Problem using the Ant Colony Optimization algorithm and also using the exact algorithm. The results showed that the greater the size of the Traveling Salesman Problem case, the longer the execution time required. The results also showed that the execution times of the Ant Colony Optimization are much faster than the execution time of the exact method.
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
利用蚁群优化算法确定旅行商问题路线
蚁群优化是一种用于解决组合优化问题的元启发式方法。蚁群优化算法是受现实世界中蚂蚁行为的启发,建立食物源和巢穴之间的最短路径。旅行商问题是一个最优化问题。旅行推销员问题是一个寻找从初始节点到整个节点的最小距离的问题,每个节点必须被访问一次并且必须返回到初始节点。旅行商问题是一个非确定性多项式时间完全问题。本研究讨论了用蚁群优化算法求解旅行商问题,同时也使用了精确算法。结果表明,旅行商问题案例的规模越大,所需的执行时间越长。结果还表明,蚁群优化的执行时间比精确方法的执行时间要快得多。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
10
审稿时长
8 weeks
期刊最新文献
The role of ethical leadership in organizational culture Analysis of e-learning user satisfaction at XYZ University in the new normal era of the covid-19 pandemic The investigation of EFL teachers’ professional and social competence in english online teaching (In Utilizing ICT Media) Web based yogyakarta food recipe application using sdlc waterfall method Carimontir marketing PLAN s(motor vehicle service application)
×
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