使用粒子群算法和遗传算法的搜索方法

IF 0.1 Q4 ENGINEERING, MULTIDISCIPLINARY
Jovani Alberto Jiménez-Builes, Rafael Esteban Arango-Sanchez, Leidy Diana Jiménez-Pinzón
{"title":"使用粒子群算法和遗传算法的搜索方法","authors":"Jovani Alberto Jiménez-Builes, Rafael Esteban Arango-Sanchez, Leidy Diana Jiménez-Pinzón","doi":"10.21501/21454086.1901","DOIUrl":null,"url":null,"abstract":"This article presents the study of two metaheuristic methods based in populations, the comparison between two search algorithms, the particle swarm algorithm (PSO) and genetic algorithm (GA) for solving problems whose objective is optimize always looking for the lowest value. To carry out this study, we made an application in JAVA programming language that contains the implementation of the two algorithms to be used for evaluation of nonlinear functions. The result of this work is shown by comparing the accuracy to obtain the optimal solution of the methods listed above, showing the evolution of the results in graphical form to reach the solution. From this study it can be concluded that the particle swarm optimization has a better performance than genetic algorithm.","PeriodicalId":53826,"journal":{"name":"Revista Digital Lampsakos","volume":"1 1","pages":"52-60"},"PeriodicalIF":0.1000,"publicationDate":"2016-12-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Métodos de búsqueda usando los algoritmos de enjambre de partículas y genético\",\"authors\":\"Jovani Alberto Jiménez-Builes, Rafael Esteban Arango-Sanchez, Leidy Diana Jiménez-Pinzón\",\"doi\":\"10.21501/21454086.1901\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article presents the study of two metaheuristic methods based in populations, the comparison between two search algorithms, the particle swarm algorithm (PSO) and genetic algorithm (GA) for solving problems whose objective is optimize always looking for the lowest value. To carry out this study, we made an application in JAVA programming language that contains the implementation of the two algorithms to be used for evaluation of nonlinear functions. The result of this work is shown by comparing the accuracy to obtain the optimal solution of the methods listed above, showing the evolution of the results in graphical form to reach the solution. From this study it can be concluded that the particle swarm optimization has a better performance than genetic algorithm.\",\"PeriodicalId\":53826,\"journal\":{\"name\":\"Revista Digital Lampsakos\",\"volume\":\"1 1\",\"pages\":\"52-60\"},\"PeriodicalIF\":0.1000,\"publicationDate\":\"2016-12-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Revista Digital Lampsakos\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.21501/21454086.1901\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Revista Digital Lampsakos","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21501/21454086.1901","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 1

摘要

本文研究了两种基于种群的元启发式算法,比较了粒子群算法(PSO)和遗传算法(GA)两种搜索算法在求解目标为寻找最小值的最优化问题中的应用。为了开展这项研究,我们用JAVA编程语言编写了一个应用程序,其中包含了用于非线性函数求值的两种算法的实现。通过比较上述几种方法求得最优解的精度来显示本工作的结果,并以图形形式显示了结果达到最优解的演变过程。研究表明,粒子群算法比遗传算法具有更好的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Métodos de búsqueda usando los algoritmos de enjambre de partículas y genético
This article presents the study of two metaheuristic methods based in populations, the comparison between two search algorithms, the particle swarm algorithm (PSO) and genetic algorithm (GA) for solving problems whose objective is optimize always looking for the lowest value. To carry out this study, we made an application in JAVA programming language that contains the implementation of the two algorithms to be used for evaluation of nonlinear functions. The result of this work is shown by comparing the accuracy to obtain the optimal solution of the methods listed above, showing the evolution of the results in graphical form to reach the solution. From this study it can be concluded that the particle swarm optimization has a better performance than genetic 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
Revista Digital Lampsakos
Revista Digital Lampsakos ENGINEERING, MULTIDISCIPLINARY-
自引率
0.00%
发文量
0
审稿时长
12 weeks
×
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