新的单参数蜜蜂算法

IF 3.4 3区 医学 Q1 ENGINEERING, MULTIDISCIPLINARY Biomimetics Pub Date : 2024-10-18 DOI:10.3390/biomimetics9100634
Hamid Furkan Suluova, Duc Truong Pham
{"title":"新的单参数蜜蜂算法","authors":"Hamid Furkan Suluova, Duc Truong Pham","doi":"10.3390/biomimetics9100634","DOIUrl":null,"url":null,"abstract":"<p><p>Based on bee foraging behaviour, the Bees Algorithm (BA) is an optimisation metaheuristic algorithm which has found many applications in both the continuous and combinatorial domains. The original version of the Bees Algorithm has six user-selected parameters: the number of scout bees, the number of high-performing bees, the number of top-performing or \"elite\" bees, the number of forager bees following the elite bees, the number of forager bees recruited by the other high-performing bees, and the neighbourhood size. These parameters must be chosen with due care, as their values can impact the algorithm's performance, particularly when the problem is complex. However, determining the optimum values for those parameters can be time-consuming for users who are not familiar with the algorithm. This paper presents BA<sub>1</sub>, a Bees Algorithm with just one parameter. BA<sub>1</sub> eliminates the need to specify the numbers of high-performing and elite bees and other associated parameters. Instead, it uses incremental k-means clustering to divide the scout bees into groups. By reducing the required number of parameters, BA<sub>1</sub> simplifies the tuning process and increases efficiency. BA<sub>1</sub> has been evaluated on 23 benchmark functions in the continuous domain, followed by 12 problems from the TSPLIB in the combinatorial domain. The results show good performance against popular nature-inspired optimisation algorithms on the problems tested.</p>","PeriodicalId":8907,"journal":{"name":"Biomimetics","volume":null,"pages":null},"PeriodicalIF":3.4000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11505725/pdf/","citationCount":"0","resultStr":"{\"title\":\"A New Single-Parameter Bees Algorithm.\",\"authors\":\"Hamid Furkan Suluova, Duc Truong Pham\",\"doi\":\"10.3390/biomimetics9100634\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Based on bee foraging behaviour, the Bees Algorithm (BA) is an optimisation metaheuristic algorithm which has found many applications in both the continuous and combinatorial domains. The original version of the Bees Algorithm has six user-selected parameters: the number of scout bees, the number of high-performing bees, the number of top-performing or \\\"elite\\\" bees, the number of forager bees following the elite bees, the number of forager bees recruited by the other high-performing bees, and the neighbourhood size. These parameters must be chosen with due care, as their values can impact the algorithm's performance, particularly when the problem is complex. However, determining the optimum values for those parameters can be time-consuming for users who are not familiar with the algorithm. This paper presents BA<sub>1</sub>, a Bees Algorithm with just one parameter. BA<sub>1</sub> eliminates the need to specify the numbers of high-performing and elite bees and other associated parameters. Instead, it uses incremental k-means clustering to divide the scout bees into groups. By reducing the required number of parameters, BA<sub>1</sub> simplifies the tuning process and increases efficiency. BA<sub>1</sub> has been evaluated on 23 benchmark functions in the continuous domain, followed by 12 problems from the TSPLIB in the combinatorial domain. The results show good performance against popular nature-inspired optimisation algorithms on the problems tested.</p>\",\"PeriodicalId\":8907,\"journal\":{\"name\":\"Biomimetics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-10-18\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11505725/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Biomimetics\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.3390/biomimetics9100634\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Biomimetics","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.3390/biomimetics9100634","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

蜜蜂算法(BA)是一种基于蜜蜂觅食行为的优化元启发式算法,在连续领域和组合领域都有很多应用。蜜蜂算法的原始版本有六个用户选择参数:侦察蜜蜂的数量、高绩效蜜蜂的数量、最高绩效或 "精英 "蜜蜂的数量、跟随精英蜜蜂的觅食蜜蜂的数量、其他高绩效蜜蜂招募的觅食蜜蜂的数量以及邻域大小。这些参数的选择必须慎重,因为它们的值会影响算法的性能,尤其是在问题复杂的情况下。然而,对于不熟悉算法的用户来说,确定这些参数的最佳值可能会很耗时。本文介绍的 BA1 是一种只有一个参数的蜜蜂算法。BA1 无需指定高绩效蜜蜂和精英蜜蜂的数量以及其他相关参数。取而代之的是,它使用增量 K 均值聚类将侦察蜜蜂分成若干组。通过减少所需的参数数量,BA1 简化了调整过程并提高了效率。BA1 在连续域的 23 个基准函数上进行了评估,随后在组合域的 TSPLIB 中对 12 个问题进行了评估。结果表明,在所测试的问题上,与流行的自然启发优化算法相比,BA1 性能良好。
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
A New Single-Parameter Bees Algorithm.

Based on bee foraging behaviour, the Bees Algorithm (BA) is an optimisation metaheuristic algorithm which has found many applications in both the continuous and combinatorial domains. The original version of the Bees Algorithm has six user-selected parameters: the number of scout bees, the number of high-performing bees, the number of top-performing or "elite" bees, the number of forager bees following the elite bees, the number of forager bees recruited by the other high-performing bees, and the neighbourhood size. These parameters must be chosen with due care, as their values can impact the algorithm's performance, particularly when the problem is complex. However, determining the optimum values for those parameters can be time-consuming for users who are not familiar with the algorithm. This paper presents BA1, a Bees Algorithm with just one parameter. BA1 eliminates the need to specify the numbers of high-performing and elite bees and other associated parameters. Instead, it uses incremental k-means clustering to divide the scout bees into groups. By reducing the required number of parameters, BA1 simplifies the tuning process and increases efficiency. BA1 has been evaluated on 23 benchmark functions in the continuous domain, followed by 12 problems from the TSPLIB in the combinatorial domain. The results show good performance against popular nature-inspired optimisation algorithms on the problems tested.

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
Biomimetics
Biomimetics Biochemistry, Genetics and Molecular Biology-Biotechnology
CiteScore
3.50
自引率
11.10%
发文量
189
审稿时长
11 weeks
期刊最新文献
Brain-Inspired Architecture for Spiking Neural Networks. Explorative Binary Gray Wolf Optimizer with Quadratic Interpolation for Feature Selection. Path Planning of an Unmanned Aerial Vehicle Based on a Multi-Strategy Improved Pelican Optimization Algorithm. Performance Comparison of Bio-Inspired Algorithms for Optimizing an ANN-Based MPPT Forecast for PV Systems. Clinical Applications of Micro/Nanobubble Technology in Neurological Diseases.
×
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