Dual-path differential perturbation sand cat swarm optimization algorithm integrated with escape mechanism.

IF 1.3 4区 工程技术 Q3 INSTRUMENTS & INSTRUMENTATION
Qian Qian, Wentao Luo, Jiawen Pan, Miao Song, Yong Feng, Yingna Li
{"title":"Dual-path differential perturbation sand cat swarm optimization algorithm integrated with escape mechanism.","authors":"Qian Qian, Wentao Luo, Jiawen Pan, Miao Song, Yong Feng, Yingna Li","doi":"10.1063/5.0222940","DOIUrl":null,"url":null,"abstract":"<p><p>In this paper, based on the sand cat swarm optimization (SCSO) algorithm, a dual-path differential perturbation sand cat swarm optimization algorithm integrated with escape mechanism (EDSCSO) is proposed. EDSCSO aims to solve the problems of the original SCSO, such as the limited diversity of the population, low efficiency of solving complex functions, and ease of falling into a local optimal solution. First, an escape mechanism was proposed to balance the exploration and exploitation of the algorithm. Second, a random elite cooperative guidance strategy was used to utilize the elite population to guide the general population to improve the convergence speed of the algorithm. Finally, the dual-path differential perturbation strategy is used to continuously perturb the population using two differential variational operators to enrich population diversity. EDSCSO obtained the best average fitness for 27 of 39 test functions in the IEEE CEC2017 and IEEE CEC2019 test suites, indicating that the algorithm is an efficient and feasible solution for complex optimization problems. In addition, EDSCSO is applied to optimize the three-dimensional wireless sensor network coverage as well as the unmanned aerial vehicle path planning problem, and it provides optimal solutions for both problems. The applicability of EDSCSO in real-world optimization scenarios was verified.</p>","PeriodicalId":21111,"journal":{"name":"Review of Scientific Instruments","volume":"95 11","pages":""},"PeriodicalIF":1.3000,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Review of Scientific Instruments","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1063/5.0222940","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INSTRUMENTS & INSTRUMENTATION","Score":null,"Total":0}
引用次数: 0

Abstract

In this paper, based on the sand cat swarm optimization (SCSO) algorithm, a dual-path differential perturbation sand cat swarm optimization algorithm integrated with escape mechanism (EDSCSO) is proposed. EDSCSO aims to solve the problems of the original SCSO, such as the limited diversity of the population, low efficiency of solving complex functions, and ease of falling into a local optimal solution. First, an escape mechanism was proposed to balance the exploration and exploitation of the algorithm. Second, a random elite cooperative guidance strategy was used to utilize the elite population to guide the general population to improve the convergence speed of the algorithm. Finally, the dual-path differential perturbation strategy is used to continuously perturb the population using two differential variational operators to enrich population diversity. EDSCSO obtained the best average fitness for 27 of 39 test functions in the IEEE CEC2017 and IEEE CEC2019 test suites, indicating that the algorithm is an efficient and feasible solution for complex optimization problems. In addition, EDSCSO is applied to optimize the three-dimensional wireless sensor network coverage as well as the unmanned aerial vehicle path planning problem, and it provides optimal solutions for both problems. The applicability of EDSCSO in real-world optimization scenarios was verified.

集成逃逸机制的双路径差分扰动沙猫群优化算法。
本文在沙猫群优化算法(SCSO)的基础上,提出了一种集成逃逸机制的双路径微分扰动沙猫群优化算法(EDSCSO)。EDSCSO旨在解决原SCSO存在的问题,如种群多样性有限、求解复杂函数效率低、易陷入局部最优解等。首先,提出了一种逃逸机制,以平衡算法的探索和利用。其次,采用随机精英合作引导策略,利用精英种群引导普通种群,提高算法的收敛速度。最后,采用双路径微分扰动策略,利用两个微分变异算子对种群进行持续扰动,以丰富种群多样性。在 IEEE CEC2017 和 IEEE CEC2019 测试套件的 39 个测试函数中,EDSCSO 获得了 27 个函数的最佳平均适合度,表明该算法是复杂优化问题的高效可行解决方案。此外,EDSCSO 还被应用于优化三维无线传感器网络覆盖以及无人机路径规划问题,并为这两个问题提供了最优解。EDSCSO 在实际优化场景中的适用性得到了验证。
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 求助全文
来源期刊
Review of Scientific Instruments
Review of Scientific Instruments 工程技术-物理:应用
CiteScore
3.00
自引率
12.50%
发文量
758
审稿时长
2.6 months
期刊介绍: Review of Scientific Instruments, is committed to the publication of advances in scientific instruments, apparatuses, and techniques. RSI seeks to meet the needs of engineers and scientists in physics, chemistry, and the life sciences.
×
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