• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利：CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法，用中文像聊天一样搜索，搜遍4000万医学文献。AI智能推荐，让科研检索更轻松。

立即免费搜索

文件翻译

保留排版，准确专业，支持PDF/Word/PPT等文件格式，支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述，25分钟生成高质量综述，智能提取关键信息，辅助科研写作。

立即免费体验

用于无约束优化的自适应布谷鸟搜索算法。

Adaptive cuckoo search algorithm for unconstrained optimization.

作者信息

Ong Pauline

机构信息

Faculty of Mechanical and Manufacturing Engineering, Universiti Tun Hussein Onn Malaysia (UTHM), 86400 Parit Raja, Batu Pahat, Johor, Malaysia.

出版信息

ScientificWorldJournal. 2014;2014:943403. doi: 10.1155/2014/943403. Epub 2014 Sep 14.

DOI:10.1155/2014/943403
PMID:25298971
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC4178923/
Abstract

Modification of the intensification and diversification approaches in the recently developed cuckoo search algorithm (CSA) is performed. The alteration involves the implementation of adaptive step size adjustment strategy, and thus enabling faster convergence to the global optimal solutions. The feasibility of the proposed algorithm is validated against benchmark optimization functions, where the obtained results demonstrate a marked improvement over the standard CSA, in all the cases.

摘要

对最近开发的布谷鸟搜索算法（CSA）中的强化和多样化方法进行了修改。这种改变涉及自适应步长调整策略的实施，从而能够更快地收敛到全局最优解。针对基准优化函数验证了所提算法的可行性，在所有情况下，所获得的结果均表明相较于标准CSA有显著改进。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/cc86496a2caf/TSWJ2014-943403.alg.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/b1f74f55bf3a/TSWJ2014-943403.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/cc2d9da9aa50/TSWJ2014-943403.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/488b0696d94d/TSWJ2014-943403.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/21ad77a2498b/TSWJ2014-943403.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/e6f7decfde23/TSWJ2014-943403.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/fc5bb668c7e0/TSWJ2014-943403.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/da8a72bdb260/TSWJ2014-943403.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/9d11107c8d3c/TSWJ2014-943403.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/cc86496a2caf/TSWJ2014-943403.alg.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/b1f74f55bf3a/TSWJ2014-943403.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/cc2d9da9aa50/TSWJ2014-943403.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/488b0696d94d/TSWJ2014-943403.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/21ad77a2498b/TSWJ2014-943403.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/e6f7decfde23/TSWJ2014-943403.005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/fc5bb668c7e0/TSWJ2014-943403.006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/da8a72bdb260/TSWJ2014-943403.007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/9d11107c8d3c/TSWJ2014-943403.alg.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5ac0/4178923/cc86496a2caf/TSWJ2014-943403.alg.002.jpg

相似文献

1
Adaptive cuckoo search algorithm for unconstrained optimization.用于无约束优化的自适应布谷鸟搜索算法。
ScientificWorldJournal. 2014;2014:943403. doi: 10.1155/2014/943403. Epub 2014 Sep 14.
2
Modified Cuckoo Search Algorithm using a New Selection Scheme for Unconstrained Optimization Problems.基于新型选择策略的改进布谷鸟搜索算法在无约束优化问题中的应用。
Curr Med Imaging. 2020;16(4):307-315. doi: 10.2174/1573405614666180905111128.
3
Cuckoo Search Algorithm Based on Repeat-Cycle Asymptotic Self-Learning and Self-Evolving Disturbance for Function Optimization.基于重复循环渐近自学习和自进化扰动的布谷鸟搜索算法用于函数优化
Comput Intell Neurosci. 2015;2015:374873. doi: 10.1155/2015/374873. Epub 2015 Aug 3.
4
An improved hybrid encoding cuckoo search algorithm for 0-1 knapsack problems.一种用于0-1背包问题的改进混合编码布谷鸟搜索算法。
Comput Intell Neurosci. 2014;2014:970456. doi: 10.1155/2014/970456. Epub 2014 Jan 12.
5
An improved CS-LSSVM algorithm-based fault pattern recognition of ship power equipments.一种基于改进的CS-LSSVM算法的船舶电力设备故障模式识别方法
PLoS One. 2017 Feb 9;12(2):e0171246. doi: 10.1371/journal.pone.0171246. eCollection 2017.
6
A simulated annealing algorithm for prioritized multiobjective optimization--implementation in an adaptive model predictive control configuration.一种用于优先级多目标优化的模拟退火算法——在自适应模型预测控制配置中的实现
IEEE Trans Syst Man Cybern B Cybern. 2007 Aug;37(4):902-15. doi: 10.1109/tsmcb.2007.896015.
7
Enhanced differential evolution with adaptive strategies for numerical optimization.用于数值优化的具有自适应策略的增强差分进化算法。
IEEE Trans Syst Man Cybern B Cybern. 2011 Apr;41(2):397-413. doi: 10.1109/TSMCB.2010.2056367. Epub 2010 Sep 9.
8
An Innovative Excited-ACS-IDGWO Algorithm for Optimal Biomedical Data Feature Selection.一种创新的基于激发 ACS-IDGWO 算法的最优生物医学数据特征选择方法。
Biomed Res Int. 2020 Aug 17;2020:8506365. doi: 10.1155/2020/8506365. eCollection 2020.
9
A modified filter nonmonotone adaptive retrospective trust region method.一种改进的过滤非单调自适应回溯信赖域方法。
PLoS One. 2021 Jun 17;16(6):e0253016. doi: 10.1371/journal.pone.0253016. eCollection 2021.
10
A self-learning particle swarm optimizer for global optimization problems.一种用于全局优化问题的自学习粒子群优化器。
IEEE Trans Syst Man Cybern B Cybern. 2012 Jun;42(3):627-46. doi: 10.1109/TSMCB.2011.2171946. Epub 2011 Nov 4.

引用本文的文献

1
Cuckoo Algorithm Based on Global Feedback.基于全局反馈的布谷鸟算法。
Comput Intell Neurosci. 2023 Jan 7;2023:2040866. doi: 10.1155/2023/2040866. eCollection 2023.
2
Feature Selection on Elite Hybrid Binary Cuckoo Search in Binary Label Classification.精英混合二进制布谷鸟搜索在二进制标签分类中的特征选择。
Comput Math Methods Med. 2021 May 11;2021:5588385. doi: 10.1155/2021/5588385. eCollection 2021.
3
A Systematic and Meta-Analysis Survey of Whale Optimization Algorithm.鲸鱼优化算法的系统与元分析调查

本文引用的文献

1
Cuckoo search algorithm based satellite image contrast and brightness enhancement using DWT-SVD.基于布谷鸟搜索算法的离散小波变换-奇异值分解卫星图像对比度和亮度增强
ISA Trans. 2014 Jul;53(4):1286-96. doi: 10.1016/j.isatra.2014.04.007. Epub 2014 Jun 2.
Comput Intell Neurosci. 2019 Apr 28;2019:8718571. doi: 10.1155/2019/8718571. eCollection 2019.
4
Hybridization of Strength Pareto Multiobjective Optimization with Modified Cuckoo Search Algorithm for Rectangular Array.用于矩形阵列的强度帕累托多目标优化与改进布谷鸟搜索算法的杂交
Sci Rep. 2017 Apr 20;7:46521. doi: 10.1038/srep46521.