Quantum-Inspired Electromagnetism-Like Mechanism for Solving 0/1 Knapsack Problem

Chih-Cheng Chang, Chi-Yuan Chen, Cheng-Wei Fan, H. Chao, Yao-Hsin Chou
{"title":"Quantum-Inspired Electromagnetism-Like Mechanism for Solving 0/1 Knapsack Problem","authors":"Chih-Cheng Chang, Chi-Yuan Chen, Cheng-Wei Fan, H. Chao, Yao-Hsin Chou","doi":"10.1109/ITCS.2010.5581278","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a novel evolutionary computing method which is called quantum-inspired electromagnetism-like mechanism (QEM) to solve 0/1 knapsack problem. QEM is based on the electromagnetism theory and using the characteristic of quantum computing. It can rapidly and efficiently find out the optimal solution of combination optimization problem. We compare the conventional genetic algorithm (CGA), quantum-inspired genetic algorithm (QGA), quantum-inspired electromagnetism-like mechanism algorithm (QEM). The experiment results show that the QEM is better than CGA, EM and QGA in general cases.","PeriodicalId":166169,"journal":{"name":"2010 2nd International Conference on Information Technology Convergence and Services","volume":"27 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 2nd International Conference on Information Technology Convergence and Services","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ITCS.2010.5581278","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6

Abstract

In this paper, we propose a novel evolutionary computing method which is called quantum-inspired electromagnetism-like mechanism (QEM) to solve 0/1 knapsack problem. QEM is based on the electromagnetism theory and using the characteristic of quantum computing. It can rapidly and efficiently find out the optimal solution of combination optimization problem. We compare the conventional genetic algorithm (CGA), quantum-inspired genetic algorithm (QGA), quantum-inspired electromagnetism-like mechanism algorithm (QEM). The experiment results show that the QEM is better than CGA, EM and QGA in general cases.
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
求解0/1背包问题的量子启发类电磁学机制
本文提出了一种新的进化计算方法——量子启发类电磁机制(QEM)来求解0/1背包问题。量子力学以电磁学理论为基础,利用了量子计算的特性。它能快速有效地找出组合优化问题的最优解。比较了传统遗传算法(CGA)、量子启发遗传算法(QGA)和量子启发类电磁机制算法(QEM)。实验结果表明,在一般情况下,QEM算法优于CGA、EM算法和QGA算法。
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
0
期刊最新文献
Context Based Data Verifying Method in Ubiquitous Computing Environment Anonymous Access Control Framework Based on Group Signature Data Mining in Personalized Travel Information System A Fast Test Architecture for Asynchronous Network-on-Chip Routing Networks Enhancing Network Availability by Tolerance Control in Multi-Sink Wireless Sensor Network
×
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