检索结果-内蒙古大学图书馆

A hybrid butterfly and Newton-Raphson swarm intelligence algorithm based on opposition-based learning

CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2024年第10期27卷 14469-14514页

作者： Li, Chuan Zhu, Yanjie Xian Univ Posts & Telecommun Sch Comp Xian 710121 Shaanxi Peoples R China

In response to the issues of local optima entrapment, slow convergence, and low optimization accuracy in butterfly optimization algorithm (BOA), this paper proposes a hybrid butterfly and Newton-Raphson swarm intelligence algorithm based on Opposition-based learning (BOANRBO). Firstly, by Opposition-based learning, the initialization strategy of the butterfly algorithm is improved to accelerate convergence. Secondly, adaptive perception modal factors are introduced into the original butterfly algorithm, controlling the adjustment rate through the adjustment factor alpha to enhance the algorithm's global search capability. Then, the exploration probability p\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$p$$\end{document} is dynamically adjusted based on the algorithm's runtime, increasing or decreasing exploration probability by examining changes in fitness to achieve a balance between exploration and exploitation. Finally, the exploration capability of BOA is enhanced by incorporating the Newton-Raphson-based optimizer (NRBO) to help BOA avoid local optima traps. The optimization performance of BOANRBO is evaluated on 65 standard benchmark functions from CEC-2005, CEC-2017, and CEC-2022, and the obtained optimization results are compared with the performance of 17 other well-known algorithms. Simulation results indicate that in the 12 test functions of CEC-2022, the BOANRBO algorithm achieved 8 optimal results (66.7%). In CEC-2017, out of 30 test functions, it obtained 27 optimal results (90%). In CEC-2005, among 23 test functions, it secured 22 optimal results (95.6%). Additionally, experiments have validated the algorithm's practicality and superior performance in 5 engineering design optimization problems and 2 real-world problems.

关键词： butterfly optimization algorithm Newton-Raphson-based optimizer Opposition-based learning Adaptive perception modal factor Dynamic exploration probability

来源：评论

学校读者我要写书评

暂无评论

Two-tier nature inspired optimization-driven ensemble of deep learning models for effective autism spectrum disorder diagnosis in disabled persons

引用

SCIENTIFIC REPORTS 2025年第1期15卷 1-23页

作者： Alotaibi, Saud S. Alghamdi, Turki Ali Alharthi, Reem Umm Al Qura Univ Coll Comp Dept Comp Sci & Artificial Intelligence Mecca Saudi Arabia Univ Hafr AlBatin Appl Coll Dept Nondestruct Evaluat POB 1803 Hafr AlBatin 39524 Saudi Arabia King Salman Ctr Disabil Res Riyadh 11614 Saudi Arabia

Autism spectrum disorder (ASD) includes a varied set of neuropsychiatric illnesses. This disorder is described by a definite grade of loss in social communication, academic functioning, personal contact, and limited and repetitive behaviours. Individuals with ASD might perform, convey, and study in a different way than others. ASDs naturally are apparent before age 3 years, with related impairments affecting manifold regions of a person's lifespan. Deep learning (DL) and machine learning (ML) techniques are used in medical research to diagnose and detect ASD promptly. This study presents a Two-Tier Metaheuristic-Driven Ensemble Deep Learning for Effective Autism Spectrum Disorder Diagnosis in Disabled Persons (T2MEDL-EASDDP) model. The main aim of the presented T2MEDL-EASDDP model is to analyze and diagnose the different stages of ASD in disabled individuals. To accomplish this, the T2MEDL-EASDDP model utilizes min-max normalization for data pre-processing to ensure that the input data is scaled to a uniform range. Furthermore, the improved butterfly optimization algorithm (IBOA)-based feature selection (FS) is utilized to identify the most relevant features and reduce dimensionality efficiently. Additionally, an ensemble of DL holds three approaches, namely autoencoder (AE), long short-term memory (LSTM), and deep belief network (DBN) approach is employed for analyzing and detecting ASD. Finally, the presented T2MEDL-EASDDP model employs brownian motion (BM) and directional mutation scheme-based coati optimizer algorithm (BDCOA) techniques to fine-tune the hyperparameters involved in the three ensemble methods. A wide range of simulation analyses of the T2MEDL-EASDDP technique is accomplished under the ASD-Toddler and ASD-Adult datasets. The performance validation of the T2MEDL-EASDDP method portrayed a superior accuracy value of 97.79% over existing techniques.

关键词： Autism spectrum disorder Metaheuristic Disabled persons butterfly optimization algorithm Deep learning

来源：评论

学校读者我要写书评

暂无评论

MIMI-ONET: Multi-Modal image augmentation via butterfly Optimized neural network for Huntington Disease Detection

引用

BRAIN RESEARCH 2025年 1855卷 149530-149530页

作者： Amudaria, S. Jawhar, S. Joseph Arunachala Coll Engn Women Dept Comp Sci & Engn Nagercoil Tamil Nadu India Arunachala Coll Engn Women Dept Elect & Elect Engn Nagercoil Tamil Nadu India

Huntington's disease (HD) is a chronic neurodegenerative ailment that affects cognitive decline, motor impairment, and psychiatric symptoms. However, the existing HD detection methods are struggle with limited annotated datasets that restricts their generalization performance. This research work proposes a novel MIMIONET for primary detection of HD using augmented multi-modal brain MRI images. The two-dimensional stationary wavelet transform (2DSWT) decomposes the MRI images into different frequency wavelet sub-bands. These sub-bands are enhanced with Contract Stretching Adaptive Histogram Equalization (CSAHE) and Multiscale Adaptive Retinex (MSAR) by reducing the irrelevant distortions. The proposed MIMI-ONET introduces a Hepta Generative Adversarial Network (Hepta-GAN) to generates different noise-free HD images based on hepta azimuth angles (45 degrees, 90 degrees, 135 degrees, 180 degrees, 225 degrees, 270 degrees, 315 degrees). Hepta-GAN incorporates Affine Estimation Module (AEM) to extract the multi-scale features using dilated convolutional layers for efficient HD image generation. Moreover, Hepta-GAN is normalized with butterfly optimization (BO) algorithm for enhancing augmentation performance by balancing the parameters. Finally, the generated images are given to Deep neural network (DNN) for the classification of normal control (NC), Adult-Onset HD (AHD) and Juvenile HD (JHD) cases. The ability of the proposed MIMI-ONET is evaluated with precision, specificity, f1 score, recall, and accuracy, PSNR and MSE. From the experimental results, the proposed MIMI-ONET attains the accuracy of 98.85% and reaches PSNR value of 48.05 based on the gathered Image-HD dataset. The proposed MIMI-ONET increases the overall accuracy of 9.96%, 1.85%, 5.91%, 13.80% and 13.5% for 3DCNN, KNN, FCN, RNN and ML framework respectively.

关键词： Huntington disease 2D Stationary wavelet transform Deep neural network Generative Adversarial Network butterfly optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Development of a novel RBFNN-trained nonlinear channel equalizer based on GDEBOA technique

引用

INTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING 2023年第9期37卷 2522-2544页

作者： Mohapatra, Pradyumna Kumar Rout, Saroja Kumar Nayak, Manoj Kumar Vedang Inst Technol Dept ETC Bhubaneswar Odisha India Vardhaman Coll Engn Autonomous Dept Informat Technol Hyderabad India Siksha O Anusandhan Deemed be Univ Dept Mech Engn ITER Bhubaneswar Odisha India Vedang Inst Technol Dept ETC Bhubaneswar 752010 Odisha India

The equalization of digital channels is generally known to be a nonlinear classification problem. Applications such as these can benefit from networks that approximate nonlinear mappings. It gets good performance by adjusting only one coefficient and one center closest to the input vector of the radial basis function network (RBFN), which is a simplified version of stochastic gradient techniques. Artificial Neural Networks (ANNs) are suitable for channel equalization because they have the capability to map between variables. Having only one hidden layer in Radial Basis Function Neural Network (RBFNN) makes it the most preferred equalizer to mitigate distortions in the channels. The ability to equalize nonlinear channels is strength of radial RBFNN, which is a simplified version of stochastic gradient techniques. The conventional "hit and trial" approach poses the greatest difficulty when designing RBFNN Equalizers. As a solution to these limitations, this work suggests a training strategy based on Gaussian distribution estimation strategy (GDE) in hybrid butterfly optimization algorithm (GDEBOA) for RBFNN channel equalizer. The proposed RBFNN equalizer is being trained using a population-based optimization algorithm. An algorithm, called GDEBOA, based on a GDE approach is developed in this paper. The proposed training scheme significantly outperforms existing metaheuristic algorithms in terms of mean square error (MSE) and bit error rate (BER). Furthermore, based on burst error scenarios and bit error probability (BEP), the proposed method has demonstrated greater robustness than other methods when faced with such scenarios. The effectiveness of the suggested scheme over a wide range of signal-to-noise ratios has been validated by several simulation studies. Additionally, statistical importance of the suggested technique is analyzed. It is visualized that GDEBOA performs better than existing algorithms for training RBFNN equalizer.

关键词： butterfly optimization algorithm channel equalization RBFNN

来源：评论

学校读者我要写书评

暂无评论

Intrusion Detection System with an Ensemble Learning and Feature Selection Framework for IoT Networks

引用

IETE JOURNAL OF RESEARCH 2023年第12期69卷 8859-8875页

作者： Rohini, G. Gnana Kousalya, C. Bino, J. St Josephs Inst Technol Dept ECE Chennai 600119 Tamil Nadu India

The Internet of Things (IoT) and its applications are currently the most popular research areas. The properties of IoT are easily adapted to real-life applications but they disclose threats. In computer security, the Intrusion Detection System (IDS) plays an essential role in identifying and repealing malicious deeds in computer networks. The main purpose of this work is motivated by IoT security enhancement for IDS development using ensemble learning and proposing suitable methods for classifier performance. Initially, the preprocessing strategy is used for data cleaning, encoding and normalization, which are conducted in the RPL-NIDDS17 dataset. After that, the Synthetic Minority Oversampling Technique (SMOTE) is used to balance the dataset. Secondly, the Convolution Neural Network (CNN) has been used to extract the features from the dataset. From the extracted features, the optimal features are selected by the proposed Arithmetic optimization algorithm (AOA). Finally, it is applied to the proposed weighted majority voting classifier. The AOA with the butterfly optimization algorithm (BOA) is utilized to integrate the predictions of different classifiers to select the most vote class. This enhances the chances of perceived RF, kNN, SVM kernel, Bi-LSTM and GRU classifiers. The proposed method experiment is conducted in the MATLAB platform with the RPL-NIDDS17 dataset. The proposed scheme shows better performances in terms of accuracy, error, sensitivity, specificity, FPR, F1_score, Kappa and MCC, which are compared with the existing methods and algorithms.

关键词： Arithmetic optimization algorithm butterfly optimization algorithm ensemble classifier Internet of Things intrusion detection system RPL-NIDDS17

来源：评论

学校读者我要写书评

暂无评论

Diabetic retinopathy detection by optimized deep learning model

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第18期82卷 27949-27971页

作者： Rachapudi, Venubabu Rao, K. Subba Rao, T. Subha Mastan Dileep, P. Deepika Roy, T. L. Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vaddeswaram Guntur Andhra Pradesh India B V Raju Inst Technol Dept Comp Sci & Engn Medak Telangana India CMR Tech Campus Dept Comp Sci & Engn Hyderabad Telangana India Malla Reddy Coll Engn & Technol Dept Comp Sci & Engn Kompally Hyderabad Telangana India CMR Tech Campus Dept Comp Sci & Engn AI&ML Hyderabad Telangana India

In the medical image analysis, the diagnosis of diabetic retinopathy (DR) from fundus images are identified as an open challenge and requires possible solutions. The major stages of the proposed DR are Pre-processing, Segmentation, Feature Extraction, and Classification. In Pre-processing, the retinal fundus images are RGB images, among them the G-channel is selected. Following that, histogram equalization and contrast limited adaptive histogram equalization (HE and CLAHE) are applied. Then the next stage is removing the optic disc (OD) and it is done by Circle Hough Transform (CHT). Then, the Gray Level thresholding is used for removing the blood vessels. Then the Exudates are segmented by the Modified Expectation Maximization (MEM) algorithm. Then Gray Level Co-occurrence Matrix (GLCM) is used for feature extraction. At last, features are classified by the Deep Neural Network with a butterfly optimization algorithm (DNN-BOA) classifier which is used for classifying the several stages of DR. The proposed scheme is implemented on MATLAB 2021a. The performance of the implemented of the proposed scheme is compared with the other approaches with some measures like precision, accuracy, sensitivity, F-score and specificity on the DIARETDB1 and MESSIDOR datasets. The accuracy of the proposed scheme is 0.983 and 0.989 on the two datasets respectively. The accuracy of the proposed scheme is 25.9%, 23.29%, 14.5% and 16.6% better than the approaches like KNN, SVM, DNN and DBN on the MESSIDOR dataset.

关键词： Diabetic retinopathy (DR) butterfly optimization algorithm Deep neural network (DNN) Histogram equalization (HE) Modified expectation maximization (MEM) algorithm Fundus image

来源：评论

学校读者我要写书评

暂无评论

Hybrid BOA-GWO-PSO algorithm for mitigation of congestion by optimal reactive power management

引用

OPTIMAL CONTROL APPLICATIONS & METHODS 2023年第2期44卷 935-966页

作者： Badi, Manjulata Mahapatra, Sheila Raj, Saurav Alliance Univ Dept Elect & Elect Engn Bangalore Karnataka India Inst Chem Technol Dept Elect Engn Marathwada Campus Jalna India

The generation of power with load optimization, particularly in the current deregulated electricity market conditions, is a very important process for improved planning and operation of the grid. In addition, it is very important for the system not to experience problems due to congestion, have tensile stability, and protection to increase the share of electricity from renewable sources with the current supply system. This article presents load balancing with the butterfly optimization algorithm (BOA) in a hybridized form to minimize and maximize loads when used in pool and hybrid markets. The methods have been designed to prevent the drawbacks of BOA and generate a better trade-off between exploration and exploitation abilities by hybridizing it with particle swarm optimization (PSO) and gray wolf optimizer (GWO). Empirical research on other algorithms shows that proposed hybrid BOA-GWO-PSO algorithm performs better and shows potential in diverse problems. These studies give it a significant advantage over BOA in general, and when it is employed to solve complex optimization problems validated on benchmark IEEE 30 bus system. A comparative analysis has been conducted to validate the potency of the hybrid BOA-GWO-PSO approach with some conventional meta-heuristic algorithms. Analysis of results by mathematical validation on 23 benchmark functions and application in congestion management by optimal reactive power management (RPM) reveal that the proposed technique has the potent to solve real world optimization problems and is competitive with recent methods reported in state-of- art literature.

关键词： active power loss butterfly optimization algorithm congestion management optimization reactive power management

来源：评论

学校读者我要写书评

暂无评论

Innovation network output prediction model under small sample

引用

WIRELESS NETWORKS 2023年 1-12页

作者： Zhong, Ruiqiong Chen, Weili Wang, Dong Yang, Minqiong Feng, Gege Guangdong Univ Foreign Studies Sch Informat Sci & Technol Guangzhou 510006 Peoples R China Guangzhou Univ Sch Management Guangzhou 510006 Peoples R China

As regards the innovation network output prediction, there are problems of poor generalization ability and low prediction accuracy due to the small sample size and the missing data in Industry-University-Research Cooperation. As a result, the Innovation Network Prediction Model (INPM) is proposed to improve the above-mentioned problems. Firstly, according to butterfly optimization algorithm (BOA), three strategies are proposed including particle swarm optimization, dynamic switching probability, and multiple stages perturbation based on Cauchy-Gaussian Mutation. Secondly, this paper proposes particle dynamic multiple stages perturbation_butterfly optimization algorithm (PDM_BOA);and again, by combining support vector regression (SVR) with PDM_BOA, the INPM is developed. Finally, the simulation results show that the INPM has better generalization ability and higher prediction accuracy. The results also indicate that the optimization accuracy of BOA is dramatically improved by PDM.

关键词： Innovation output Small-sample prediction butterfly optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimal reactive power management through a hybrid BOA-GWO-PSO algorithm for alleviating congestion

引用

INTERNATIONAL JOURNAL OF SYSTEM ASSURANCE ENGINEERING AND MANAGEMENT 2023年第4期14卷 1437-1456页

作者： Badi, Manjulata Mahapatra, Sheila Alliance Univ ACED EEE Dept Bangalore India

In today's deregulated energy market, improving grid management in generating power with load optimization is a critical challenge. It is also perilous that the system does not have any problems owing to transmission line clogs. The butterfly optimization algorithm is used for load balancing and load optimization in electricity markets. The proposed approach integrates Particle Swarm optimization and Grey Wolf Optimizer, merging them with butterfly optimization algorithm as a hybridised form to enhance exploration and exploitation skills. The benefit of the butterfly optimization algorithm in general, as well as when it is employed to address difficult optimization issues, is validated using the New England 39 bus test system. The amalgamated algorithm approach was compared to other established meta-heuristic algorithms for the reactive power management under variable loading conditions. Using the realistic New England 39 bus system, the suggested algorithm minimizes transmission losses by 6.344% and operating costs by 6.347% with respect to the base case, respectively. The research work reveals that proposed amalgamated algorithm employing butterfly optimization algorithm, Grey Wolf Optimizer, and Particle Swarm optimization performs better and offers more potential in a range of situations. The proposed technique mathematical validation indicated that it has the capacity to tackle complex optimization issues and compete with contemporary peer-reviewed literature solutions.

关键词： Congestion management Reactive power management optimization Real power loss butterfly optimization algorithm Hybridization algorithm

来源：评论

学校读者我要写书评

暂无评论

OTFS-NOMA System Resource Allocation Scheme Based on SADEBOA algorithm 36

OTFS-NOMA System Resource Allocation Scheme Based on SADEBOA...

引用

36th Chinese Control and Decision Conference (CCDC)

作者： Hao, Kaicheng Yang, Yongli Wuhan Univ Sci & Technol Sch Informat Sci & Engn Wuhan Peoples R China

ISBN: (纸本)9798350387780;9798350387797

This paper investigates the power allocation problem in the downlink of an Orthogonal Time Frequency Space-Non-Orthogonal Multiple Access (OTFS-NOMA) system under high-speed conditions, with the objective of maximizing system capacity and rate. A novel butterfly optimization algorithm based on Differential Evolution (DE) and Simulated Annealing (SA), referred to as SADEBOA, is proposed. In the global search phase, SADEBOA incorporates DE to enhance the global search capability of the butterfly optimization algorithm (BOA), mutating the optimal individual and guiding the population towards the optimal individual's location. In the local search phase, SADEBOA combines SA to accept a local optimal solution as a new solution with a certain probability, introducing a certain bias that is more likely to approach the optimal solution. Comparative simulations are conducted with BOA, MSBOA, DA, and RSO algorithms, as well as traditional power allocation schemes. The simulation results demonstrate that, compared to the baseline BOA algorithm, MSBOA, DA, and RSO algorithms, SADEBOA achieves performance improvements of approximately 15%, 5.3%, 21.2%, and 28.4%, in enhancing OTFS-NOMA system capacity.

关键词： orthogonal time frequency space non-orthogonal multiple access butterfly optimization algorithm resource allocation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：