Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Sampling Plans01:23

Sampling Plans

1.1K
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
1.1K
Optimal Foraging00:48

Optimal Foraging

14.1K
How animals obtain and eat their food is called foraging behavior. Foraging can include searching for plants and hunting for prey and depends on the species and environment.
14.1K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

376
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
376
Cluster Sampling Method01:20

Cluster Sampling Method

15.3K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
15.3K
Random Sampling Method01:09

Random Sampling Method

15.4K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
15.4K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.6K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.6K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Cellular Heterogeneity During Arterial Aging.

Aging cell·2026
Same author

Molecular Engineering of Vibronic Coupling Enables High-Temperature Solar-Thermal Conversion in an Organic Material.

Angewandte Chemie (International ed. in English)·2026
Same author

Safe or risky products: A systematic review on the environmental risk assessment of recycled products from waste.

Journal of hazardous materials·2026
Same author

Leveraging longitudinal data to boost statistical power for gene-environment interaction analysis.

Nature computational science·2026
Same author

Selenium-enriched strawberry-sea buckthorn-yam juice fermented by <i>Weissella cibaria</i> and <i>Pediococcus acidilactici</i>: antioxidant, flavor, and immunomodulatory effects.

Food chemistry: X·2026
Same author

Ti<sub>3</sub>C<sub>2</sub>T<sub><i>x</i></sub> MXene Nanosheets: Bridging High-Performance Energy Storage and Comprehensive In Vivo Biocompatibility Assessment.

ACS applied materials & interfaces·2026
Same journal

Computing Optimal Populations for Binary Problems using Logic Minimization.

Evolutionary computation·2026
Same journal

Enhancing Generalization and Scalability for Multi-Objective Optimization with Population Pre-Training.

Evolutionary computation·2026
Same journal

XCS for Sequential Perceptual Aliasing in Multi-Step Decision Making.

Evolutionary computation·2026
Same journal

Adapting MOEA/D to CMA-ES for Dealing with Ill-conditioned Multiobjective Problems.

Evolutionary computation·2026
Same journal

Editorial of the Special Issue: Parallel Problem Solving from Nature PPSN 2024 Extended Versions of Best Paper Candidates.

Evolutionary computation·2026
Same journal

Adaptive Sampled Walk: A Simple and Efficient Autonomous Local Search.

Evolutionary computation·2026
查看所有相关文章

相关实验视频

Updated: Mar 6, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K

一个动态的多目标进化算法,使用双空间预测和基于替代品的采样.

Tianyu Liu1, Xiangfei Wu2, He Xu3

  • 1School of Information Engineering, Shanghai Maritime University, Shanghai, 201306, China liuty@shmtu.edu.cn.

Evolutionary computation
|March 4, 2026
PubMed
概括
此摘要是机器生成的。

本研究介绍了DS-DMOEA,这是一个用于动态多目标优化问题的高级算法. 它有效地跟踪在不断变化的环境中的帕雷托最佳解决方案,使用双空间预测和基于替代品的采样.

关键词:
动态的多目标进化算法.双空间预测的预测基于代用人的采样采样.

更多相关视频

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

756
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

12.3K

相关实验视频

Last Updated: Mar 6, 2026

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
11:53

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

13.5K
Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow
08:58

Efficient Sampling of Genetically Encoded Biosensor Design Space Enabled with a Design of Experiments and Automation Workflow

Published on: October 17, 2025

756
The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy
11:53

The Modular Design and Production of an Intelligent Robot Based on a Closed-Loop Control Strategy

Published on: October 14, 2017

12.3K

科学领域:

  • 优化优化 优化优化
  • 进化计算是一种进化计算.
  • 人工智能的人工智能

背景情况:

  • 动态多目标优化问题 (DMOEPs) 需要算法来跟踪环境变化中的帕雷托最佳解决方案.
  • 现有的基于预测的动态多目标进化算法 (DMOEA) 经常使用单空间预测或对决策和目标空间的相同模型,限制了复杂动态中的有效性.
  • 由于过度的功能评估,DMOEA中的采样方法可能会导致显著的计算负担.

研究的目的:

  • 提出一种新的动态多目标进化算法 (DS-DMOEA),能够有效地适应环境变化.
  • 解决现有的DMOEA在捕捉不同的空间动态和管理计算负载方面的局限性.

主要方法:

  • DS-DMOEA采用双空间预测策略:用于目标空间的基于重量向量的方法和用于决策空间的地理流核方法.
  • 基于代用品的采样策略被用于通过在历史数据上训练代用模型来生成新环境的高质量初始种群.
  • 预测和采样的种群结合起来,形成一个针对不断变化的环境优化的初始种群.

主要成果:

  • 在19个基准问题上,DS-DMOEA与9个最先进的DMOEA进行了严格的测试.
  • 该算法在三个不同的环境变化模式中证明了它的有效性.
  • 实验结果验证了拟议的DS-DMOEA的优越性能.

结论:

  • 拟议的DS-DMOEA通过其双空间预测和基于替代品的采样策略,有效地适应动态的多目标优化问题.
  • 该算法克服了现有方法的局限性,通过在决策和目标空间中捕捉复杂的动态,同时管理计算成本.
  • DS-DMOEA 在有效处理动态多目标优化挑战方面取得了重大进展.