相关实验视频

Updated: May 10, 2025

Flying Insect Detection and Classification with Inexpensive Sensors

Flying Insect Detection and Classification with Inexpensive Sensors

Published on: October 15, 2014

改进的黑翼风算法与多策略优化,用于识别Dendrobium huoshanense

Chaochuan Jia^1,2,3, Ting Yang⁴, Maosheng Fu^1,2

¹College of Electronics and Information Engineering, West Anhui University, Lu'an 237012, China.

Biomimetics (Basel, Switzerland)

|April 25, 2025

概括

这项研究引入了一种增强的黑翼风算法 (BKAIM),该算法可以改善最初的种群质量和多样性. 在优化任务和实际应用中,BKAIM算法表现出卓越的性能.

科学领域:

计算智能是一种计算智能.
优化算法优化算法
机器学习机器学习

背景情况:

原始的黑翼风优化算法 (BKA) 由于初始种群质量低,因此受到了有限的搜索能力的影响.
在BKA中减少的人口多样性是由于迁移期间盲目遵循行为引起的,这阻碍了最佳的勘探-开发平衡.

研究的目的:

通过解决最初人口质量和人口多样性的局限性来增强黑翼优化算法 (BKA).
为了提高算法的融合精度,勘探-开发平衡,以及整体搜索能力.
验证增强算法的对基准函数和实际应用的有效性.

主要方法:

纳入基于对立的学习,以实现更高质量的初始人口生成.
在迁移期间整合了差异性突变策略,以减轻盲目领袖跟随和加强信息交换.
用随机边界方法取代吸收边界方法,以增加人口多样性.

主要成果:

改进的算法 (BKAIM) 在CEC2017,CEC2019,CEC2021和CEC2022基准函数中显示出卓越的融合性能和稳定性.
威尔科克森的等级总和测试比较证实了BKAIM与其他算法相比的增强性能.
BKAIM成功优化了一个支持向量机 (SVM) 模型,用于使用近红外光谱数据对Dendrobium huoshanense进行分级.

结论:

拟议的BKAIM显著克服了原来的BKA的局限性,提供了改进的优化功能.
对于复杂的优化问题,BKAIM提供了强大而有效的解决方案.
通过成功优化用于植物识别的SVM模型来证实算法的实际适用性.

关键词:

黑翼风优化算法黑翼风优化算法不同突变的差异突变.已经确定了Dendrobium huoshanense的种类.基于反对的学习是基于反对的学习.随机边界的边界是随机的

更多相关视频

Visually Sexing Loggerhead Shrike Lanius Ludovicianus Using Plumage Coloration and Pattern

Visually Sexing Loggerhead Shrike Lanius Ludovicianus Using Plumage Coloration and Pattern

Published on: March 8, 2020

Spotting Cheetahs: Identifying Individuals by Their Footprints

Spotting Cheetahs: Identifying Individuals by Their Footprints

Published on: May 1, 2016

相关概念视频

Optimal Foraging

Optimal Foraging

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.

Limits to Natural Selection

Limits to Natural Selection

Organisms that are well-adapted to their environment are more likely to survive and reproduce. However, natural selection does not lead to perfectly adapted organisms. Several factors constrain natural selection.

Frequency-dependent Selection

Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.

Conservation of Declining Populations

Conservation of Declining Populations

Conservation of declining population focuses on ways of detecting, diagnosing, and halting a population decline. The approach uses methods to prevent populations from going extinct.

Types of Selection

Types of Selection

Natural selection influences the frequencies of particular alleles and phenotypes within populations in several different ways. Primarily, natural selection can be directional, stabilizing, or disruptive. Directional selection favors one extreme trait and shifts the population towards that phenotype while selecting against individuals displaying alternate traits. Stabilizing selection favors an intermediate trait with a narrow range of variation. Deviation from the optimal phenotype towards an...

Inclusive Fitness

Inclusive Fitness

Most altruistic behavior—in which one animal helps another at a cost to themselves—occurs between relatives. Scientists think these altruistic behaviors evolved because they increase the inclusive fitness of the animal providing help.