社会网络优化——一般优化问题的一种新的启发式算法

H. Sherafat
{"title":"社会网络优化——一般优化问题的一种新的启发式算法","authors":"H. Sherafat","doi":"10.7198/GEINTEC.V7I4.1108","DOIUrl":null,"url":null,"abstract":"In the recent years metaheuristics were studied and developed as powerful technics for hard optimization problems. Some of well-known technics in this field are: Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony Optimization, and Swarm Intelligence, which are applied successfully to many complex optimization problems. In this paper, we introduce a new metaheuristic for solving such problems based on social networks concept, named as Social Network Optimization – SNO. We show that a wide range of np-hard optimization problems may be solved by SNO.","PeriodicalId":51965,"journal":{"name":"Revista GEINTEC-Gestao Inovacao e Tecnologias","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Social Network Optimization a New Methaheuristic for General Optimization Problems\",\"authors\":\"H. Sherafat\",\"doi\":\"10.7198/GEINTEC.V7I4.1108\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In the recent years metaheuristics were studied and developed as powerful technics for hard optimization problems. Some of well-known technics in this field are: Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony Optimization, and Swarm Intelligence, which are applied successfully to many complex optimization problems. In this paper, we introduce a new metaheuristic for solving such problems based on social networks concept, named as Social Network Optimization – SNO. We show that a wide range of np-hard optimization problems may be solved by SNO.\",\"PeriodicalId\":51965,\"journal\":{\"name\":\"Revista GEINTEC-Gestao Inovacao e Tecnologias\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-12-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Revista GEINTEC-Gestao Inovacao e Tecnologias\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.7198/GEINTEC.V7I4.1108\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Revista GEINTEC-Gestao Inovacao e Tecnologias","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.7198/GEINTEC.V7I4.1108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

近年来,元启发式被研究和发展为解决硬优化问题的强大技术。该领域的一些著名技术是:遗传算法、禁忌搜索、模拟退火、蚁群优化和群智能,它们成功地应用于许多复杂的优化问题。在本文中,我们引入了一种基于社交网络概念的新的元启发式方法来解决此类问题,称为社交网络优化-SNO。我们证明了SNO可以解决一系列np难优化问题。
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
Social Network Optimization a New Methaheuristic for General Optimization Problems
In the recent years metaheuristics were studied and developed as powerful technics for hard optimization problems. Some of well-known technics in this field are: Genetic Algorithms, Tabu Search, Simulated Annealing, Ant Colony Optimization, and Swarm Intelligence, which are applied successfully to many complex optimization problems. In this paper, we introduce a new metaheuristic for solving such problems based on social networks concept, named as Social Network Optimization – SNO. We show that a wide range of np-hard optimization problems may be solved by SNO.
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
审稿时长
20 weeks
期刊最新文献
BIOSSENSORES ENZIMÁTICOS PARA DETERMINAÇÃO DE PESTICIDAS Social Network Optimization a New Methaheuristic for General Optimization Problems ESTRATÉGIAS DE DESENVOLVIMENTO SOCIOECONÔMICO: ECOSSISTEMAS DE INOVAÇÃO PARA IMPLANTAÇÃO DE SMART CITIES – ESTUDOD DE CASOS NOS ESTADOS UNIDOS, CHINA E SUÉCIA Análise da produção científica brasileira sobre seleção de fornecedores apoiada em métodos multicritério Orientação para aprendizagem, inovatividade organizacional e desempenho organizacional sob a ótica de empresas internacionalizadas
×
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