解决优化问题的生物启发算法

K. K. Mishra, S. Tiwari, A. Misra
{"title":"解决优化问题的生物启发算法","authors":"K. K. Mishra, S. Tiwari, A. Misra","doi":"10.1109/ICCCT.2011.6075211","DOIUrl":null,"url":null,"abstract":"Although a number of nature inspired algorithms exist in literature to solve optimization problems, yet there is always a need of new algorithm which can search for optimum solution in minimum time. This paper proposes a new optimization algorithm for solving optimization problems. Proposed algorithm has been compared with existing algorithm like Genetic Algorithm, Particle swarm Optimization on benchmark functions and experiments prove that proposed algorithm is better in many cases.","PeriodicalId":285986,"journal":{"name":"2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011)","volume":"75 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2011-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"33","resultStr":"{\"title\":\"A bio inspired algorithm for solving optimization problems\",\"authors\":\"K. K. Mishra, S. Tiwari, A. Misra\",\"doi\":\"10.1109/ICCCT.2011.6075211\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Although a number of nature inspired algorithms exist in literature to solve optimization problems, yet there is always a need of new algorithm which can search for optimum solution in minimum time. This paper proposes a new optimization algorithm for solving optimization problems. Proposed algorithm has been compared with existing algorithm like Genetic Algorithm, Particle swarm Optimization on benchmark functions and experiments prove that proposed algorithm is better in many cases.\",\"PeriodicalId\":285986,\"journal\":{\"name\":\"2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011)\",\"volume\":\"75 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2011-11-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"33\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICCCT.2011.6075211\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2011 2nd International Conference on Computer and Communication Technology (ICCCT-2011)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICCCT.2011.6075211","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 33

摘要

虽然文献中存在许多受自然启发的算法来解决优化问题,但总是需要能够在最短时间内搜索到最优解的新算法。本文提出了一种新的求解优化问题的优化算法。在基准函数上与遗传算法、粒子群算法等现有算法进行了比较,实验证明本文算法在很多情况下都优于现有算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A bio inspired algorithm for solving optimization problems
Although a number of nature inspired algorithms exist in literature to solve optimization problems, yet there is always a need of new algorithm which can search for optimum solution in minimum time. This paper proposes a new optimization algorithm for solving optimization problems. Proposed algorithm has been compared with existing algorithm like Genetic Algorithm, Particle swarm Optimization on benchmark functions and experiments prove that proposed algorithm is better in many cases.
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
×
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
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
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 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信