基于概率搜索策略的函数优化问题的有效算法

Lu Peng , Chaohao Sun , Wenli Wu
{"title":"基于概率搜索策略的函数优化问题的有效算法","authors":"Lu Peng ,&nbsp;Chaohao Sun ,&nbsp;Wenli Wu","doi":"10.1016/j.dsm.2022.08.002","DOIUrl":null,"url":null,"abstract":"<div><p>This paper proposes an enhanced arithmetic optimization algorithm (AOA) called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA. Furthermore, an adjustable parameter is also developed to balance the exploration and exploitation operations. In addition, a jump mechanism is included in the PSAOA to assist individuals in jumping out of local optima. Using 29 classical benchmark functions, the proposed PSAOA is extensively tested. Compared to the AOA and other well-known methods, the experiments demonstrated that the proposed PSAOA beats existing comparison algorithms on the majority of the test functions.</p></div>","PeriodicalId":100353,"journal":{"name":"Data Science and Management","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666764922000315/pdfft?md5=4a4f5d81d0c4eab41e184daef9f1971f&pid=1-s2.0-S2666764922000315-main.pdf","citationCount":"9","resultStr":"{\"title\":\"Effective arithmetic optimization algorithm with probabilistic search strategy for function optimization problems\",\"authors\":\"Lu Peng ,&nbsp;Chaohao Sun ,&nbsp;Wenli Wu\",\"doi\":\"10.1016/j.dsm.2022.08.002\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>This paper proposes an enhanced arithmetic optimization algorithm (AOA) called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA. Furthermore, an adjustable parameter is also developed to balance the exploration and exploitation operations. In addition, a jump mechanism is included in the PSAOA to assist individuals in jumping out of local optima. Using 29 classical benchmark functions, the proposed PSAOA is extensively tested. Compared to the AOA and other well-known methods, the experiments demonstrated that the proposed PSAOA beats existing comparison algorithms on the majority of the test functions.</p></div>\",\"PeriodicalId\":100353,\"journal\":{\"name\":\"Data Science and Management\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666764922000315/pdfft?md5=4a4f5d81d0c4eab41e184daef9f1971f&pid=1-s2.0-S2666764922000315-main.pdf\",\"citationCount\":\"9\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Data Science and Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666764922000315\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Data Science and Management","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666764922000315","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 9

摘要

本文提出了一种改进的算法优化算法PSAOA,该算法结合了本文提出的概率搜索策略,提高了原算法的搜索质量。此外,还开发了一种可调参数,以平衡勘探和开采作业。此外,PSAOA中还包含一个跳跃机制,以帮助个体跳出局部最优。使用29个经典基准函数对所提出的PSAOA进行了广泛的测试。实验结果表明,本文提出的PSAOA方法在大多数测试功能上优于现有的比较算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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
Effective arithmetic optimization algorithm with probabilistic search strategy for function optimization problems

This paper proposes an enhanced arithmetic optimization algorithm (AOA) called PSAOA that incorporates the proposed probabilistic search strategy to increase the searching quality of the original AOA. Furthermore, an adjustable parameter is also developed to balance the exploration and exploitation operations. In addition, a jump mechanism is included in the PSAOA to assist individuals in jumping out of local optima. Using 29 classical benchmark functions, the proposed PSAOA is extensively tested. Compared to the AOA and other well-known methods, the experiments demonstrated that the proposed PSAOA beats existing comparison algorithms on the majority of the test functions.

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
CiteScore
7.50
自引率
0.00%
发文量
0
期刊最新文献
Comparative study of IoT- and AI-based computing disease detection approaches Forecast Uncertainties Real-Time Data-Driven Compensation Scheme for Optimal Storage Control Dual-market quantitative trading: The dynamics of liquidity and turnover in financial markets A Model for Predicting Dropout of Higher Education Students Value Realization of Intelligent Emergency Management: Research Framework from Technology Enabling to Value Creation
×
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