Modified Beluga Whale Optimization with Multi-strategies for Solving Engineering Problems

IF 4.8 2区 工程技术 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Journal of Computational Design and Engineering Pub Date : 2023-10-05 DOI:10.1093/jcde/qwad089
Heming Jia, Qixian Wen, Di Wu, Zhuo Wang, Yuhao Wang, Changsheng Wen, Laith Abualigah
{"title":"Modified Beluga Whale Optimization with Multi-strategies for Solving Engineering Problems","authors":"Heming Jia, Qixian Wen, Di Wu, Zhuo Wang, Yuhao Wang, Changsheng Wen, Laith Abualigah","doi":"10.1093/jcde/qwad089","DOIUrl":null,"url":null,"abstract":"Abstract The Beluga Whale Optimization(BWO) Algorithm is a recently proposed metaheuristic optimization algorithm that simulates three behaviors: beluga whales interacting in pairs to perform mirror swimming, population sharing information to cooperate in predation and whale fall. However, the optimization performance of the BWO algorithm still needs to be improved to enhance its practicality. This paper proposes a modified beluga whale optimization(MBWO) with a multi-strategy. It was inspired by beluga whales' two behaviors: group gathering for foraging and searching for new habitats in long-distance migration. This paper proposes a group gathering strategy (GAs) and a migration strategies (Ms). The group aggregation strategy can improve the local development ability of the algorithm and accelerate the overall Rate of convergence through the group aggregation fine search; The migration strategy randomly moves towards the periphery of the population, enhancing the ability to jump out of local optima. In order to verify the optimization ability of MBWO, this article conducted comprehensive testing on MBWO using 23 benchmark functions, IEEE CEC2014, and IEEE CEC2021. The experimental results indicate that MBWO has a strong optimization ability. This paper also tests MBWO's ability to solve practical engineering optimization problems through five practical engineering problems. The final results prove the effectiveness of MBWO in solving practical engineering optimization problems.","PeriodicalId":48611,"journal":{"name":"Journal of Computational Design and Engineering","volume":null,"pages":null},"PeriodicalIF":4.8000,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Design and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/jcde/qwad089","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

Abstract

Abstract The Beluga Whale Optimization(BWO) Algorithm is a recently proposed metaheuristic optimization algorithm that simulates three behaviors: beluga whales interacting in pairs to perform mirror swimming, population sharing information to cooperate in predation and whale fall. However, the optimization performance of the BWO algorithm still needs to be improved to enhance its practicality. This paper proposes a modified beluga whale optimization(MBWO) with a multi-strategy. It was inspired by beluga whales' two behaviors: group gathering for foraging and searching for new habitats in long-distance migration. This paper proposes a group gathering strategy (GAs) and a migration strategies (Ms). The group aggregation strategy can improve the local development ability of the algorithm and accelerate the overall Rate of convergence through the group aggregation fine search; The migration strategy randomly moves towards the periphery of the population, enhancing the ability to jump out of local optima. In order to verify the optimization ability of MBWO, this article conducted comprehensive testing on MBWO using 23 benchmark functions, IEEE CEC2014, and IEEE CEC2021. The experimental results indicate that MBWO has a strong optimization ability. This paper also tests MBWO's ability to solve practical engineering optimization problems through five practical engineering problems. The final results prove the effectiveness of MBWO in solving practical engineering optimization problems.
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
基于多策略的改进白鲸优化工程问题求解
摘要白鲸优化算法(Beluga Whale Optimization Algorithm, BWO)是最近提出的一种元启发式优化算法,它模拟了三种行为:白鲸成对互动进行镜像游泳、种群共享信息合作捕食和鲸鱼摔倒。但是,BWO算法的优化性能还有待提高,以增强其实用性。提出了一种改进的多策略白鲸优化算法。它的灵感来自于白鲸的两种行为:集体觅食和长途迁徙中寻找新栖息地。提出了一种群体聚集策略(GAs)和迁移策略(Ms)。群聚集策略通过群聚集精细搜索提高了算法的局部发展能力,加快了整体收敛速度;迁移策略随机向种群外围移动,增强了跳出局部最优的能力。为了验证MBWO的优化能力,本文使用23个基准函数,IEEE CEC2014和IEEE CEC2021对MBWO进行了全面的测试。实验结果表明,MBWO具有较强的优化能力。本文还通过五个实际工程问题来检验MBWO解决实际工程优化问题的能力。最后的结果证明了MBWO在解决实际工程优化问题中的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Computational Design and Engineering
Journal of Computational Design and Engineering Computer Science-Human-Computer Interaction
CiteScore
7.70
自引率
20.40%
发文量
125
期刊介绍: Journal of Computational Design and Engineering is an international journal that aims to provide academia and industry with a venue for rapid publication of research papers reporting innovative computational methods and applications to achieve a major breakthrough, practical improvements, and bold new research directions within a wide range of design and engineering: • Theory and its progress in computational advancement for design and engineering • Development of computational framework to support large scale design and engineering • Interaction issues among human, designed artifacts, and systems • Knowledge-intensive technologies for intelligent and sustainable systems • Emerging technology and convergence of technology fields presented with convincing design examples • Educational issues for academia, practitioners, and future generation • Proposal on new research directions as well as survey and retrospectives on mature field.
期刊最新文献
Optimizing Microseismic Monitoring: A Fusion of Gaussian-Cauchy and Adaptive Weight Strategies An RNA Evolutionary Algorithm Based on Gradient Descent for Function Optimization Modified Crayfish Optimization Algorithm with Adaptive Spiral Elite Greedy Opposition-based Learning and Search-hide Strategy for Global Optimization Non-dominated sorting simplified swarm optimization for multi-objective omni-channel of pollution routing problem Generative Early Architectural Visualizations: Incorporating Architect's Style-trained Models
×
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