多峰函数优化的免疫遗传算法改进

Wang Jian-chen, Jin Zong-xin
{"title":"多峰函数优化的免疫遗传算法改进","authors":"Wang Jian-chen, Jin Zong-xin","doi":"10.3969/J.ISSN.1001-0548.2013.05.024","DOIUrl":null,"url":null,"abstract":"The biological immune system when attacked can always find the right antibodies to destroy the antigen and can maintain the diversity of antibodies. The combination of genetic and immunity in biology has been shown to be an effective approach to solving the degeneration of genetic algorithm in the late iterative optimization. According to the characteristic that the injected vaccine immune system can accomplish quickly identification the antigen, an improved immune genetic algorithm (IIGA) is proposed based on this theory for Benchmark function optimization. The results show that the IIGA can effectively prevent the algorithm degenerative during the process of optimization of the genetic algorithm, and improve the convergent speed of the algorithm.","PeriodicalId":35864,"journal":{"name":"电子科技大学学报","volume":"42 1","pages":"769-772"},"PeriodicalIF":0.0000,"publicationDate":"2013-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Improvement of Immune Genetic Algorithm for Multi-Peak Function Optimization\",\"authors\":\"Wang Jian-chen, Jin Zong-xin\",\"doi\":\"10.3969/J.ISSN.1001-0548.2013.05.024\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The biological immune system when attacked can always find the right antibodies to destroy the antigen and can maintain the diversity of antibodies. The combination of genetic and immunity in biology has been shown to be an effective approach to solving the degeneration of genetic algorithm in the late iterative optimization. According to the characteristic that the injected vaccine immune system can accomplish quickly identification the antigen, an improved immune genetic algorithm (IIGA) is proposed based on this theory for Benchmark function optimization. The results show that the IIGA can effectively prevent the algorithm degenerative during the process of optimization of the genetic algorithm, and improve the convergent speed of the algorithm.\",\"PeriodicalId\":35864,\"journal\":{\"name\":\"电子科技大学学报\",\"volume\":\"42 1\",\"pages\":\"769-772\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-10-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"电子科技大学学报\",\"FirstCategoryId\":\"1093\",\"ListUrlMain\":\"https://doi.org/10.3969/J.ISSN.1001-0548.2013.05.024\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"电子科技大学学报","FirstCategoryId":"1093","ListUrlMain":"https://doi.org/10.3969/J.ISSN.1001-0548.2013.05.024","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 1

摘要

生物免疫系统在受到攻击时总能找到合适的抗体来消灭抗原,并能保持抗体的多样性。生物学中遗传与免疫的结合已被证明是解决遗传算法在后期迭代优化中退化的有效途径。针对注射疫苗免疫系统能够快速完成抗原识别的特点,提出了一种基于该理论的改进免疫遗传算法(IIGA),用于基准函数优化。结果表明,IIGA能有效地防止遗传算法在优化过程中的算法退化,提高算法的收敛速度。
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
Improvement of Immune Genetic Algorithm for Multi-Peak Function Optimization
The biological immune system when attacked can always find the right antibodies to destroy the antigen and can maintain the diversity of antibodies. The combination of genetic and immunity in biology has been shown to be an effective approach to solving the degeneration of genetic algorithm in the late iterative optimization. According to the characteristic that the injected vaccine immune system can accomplish quickly identification the antigen, an improved immune genetic algorithm (IIGA) is proposed based on this theory for Benchmark function optimization. The results show that the IIGA can effectively prevent the algorithm degenerative during the process of optimization of the genetic algorithm, and improve the convergent speed of the algorithm.
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
电子科技大学学报
电子科技大学学报 Engineering-Electrical and Electronic Engineering
CiteScore
1.40
自引率
0.00%
发文量
7228
期刊介绍:
期刊最新文献
基于慢病毒载体进行慢性肉芽肿病(CGD)的基因治疗 基于代谢干预策略的仿生纳米金属有机框架用于协同抗肿瘤研究 KR饮食对肺癌的抑瘤效应和放疗协同作用及其机制研究 载姜黄素的新型脑靶向递药系统治疗阿尔茨海默病的研究 海胆状金纳米颗粒的形貌及掺杂位置对OLED光提取作用研究
×
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