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A hybrid identification method on butterfly optimization and differential evolution algorithm

SMART STRUCTURES AND SYSTEMS 2020年第3期26卷 345-360页

作者： Zhou, Hongyuan Zhang, Guangcai Wang, Xiaojuan Ni, Pinghe Zhang, Jian Beijing Univ Technol Minist Educ Key Lab Urban Secur & Disaster Engn Beijing 100124 Peoples R China Jiangsu Univ Fac Civil Engn & Mech Zhenjiang 212013 Jiangsu Peoples R China

Modem swarm intelligence heuristic search methods are widely applied in the field of structural health monitoring due to their advantages of excellent global search capacity, loose requirement of initial guess and ease of computational implementation etc. To this end, a hybrid strategy is proposed based on butterfly optimization algorithm (BOA) and differential evolution (DE) with purpose of effective combination of their merits. In the proposed identification strategy, two improvements including mutation and crossover operations of DE, and dynamic adaptive operators are introduced into original BOA to reduce the risk to be trapped in local optimum and increase global search capability. The performance of the proposed algorithm, hybrid butterfly optimization and differential evolution algorithm (HBODEA) is evaluated by two numerical examples of a simply supported beam and a 37-bar truss structure, as well as an experimental test of 8-story shear-type steel frame structure in the laboratory. Compared with BOA and DE, the numerical and experimental results show that the proposed HBODEA is more robust to detect the reduction of stiffness with limited sensors and contaminated measurements. In addition, the effect of search space, two dynamic operators, population size on identification accuracy and efficiency of the proposed identification strategy are further investigated.

关键词： butterfly optimization algorithm differential evolution hybrid algorithm structural damage detection parameter identification dynamic adaptive operator

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Optimized automated detection of diabetic retinopathy severity: integrating improved multithresholding tunicate swarm algorithm and improved hybrid butterfly optimization

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HEALTH INFORMATION SCIENCE AND SYSTEMS 2024年第1期12卷 42页

作者： Bhimavarapu, Usharani Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Vijayawada Andhra Prades India

Diabetic retinopathy, a complication of diabetes, damages the retina due to prolonged high blood sugar levels, leading to vision impairment and blindness. Early detection through regular eye exams and proper diabetes management are crucial in preventing vision loss. DR is categorized into five classes based on severity, ranging from no retinopathy to proliferative diabetic retinopathy. This study proposes an automated detection method using fundus images. Image segmentation divides fundus images into homogeneous regions, facilitating feature extraction. Feature selection aims to reduce computational costs and improve classification accuracy by selecting relevant features. The proposed algorithm integrates an Improved Tunicate Swarm algorithm (ITSA) with Renyi's entropy for enhanced adaptability in the initial and final stages. An Improved Hybrid butterfly optimization (IHBO) algorithm is also introduced for feature selection. The effectiveness of the proposed method is demonstrated using retinal fundus image datasets, achieving promising results in DR severity classification. For the IDRiD dataset, the proposed model achieves a segmentation Dice coefficient of 98.06% and classification accuracy of 98.21%. In contrast, the E-Optha dataset attains a segmentation Dice coefficient of 97.95% and classification accuracy of 99.96%. Experimental results indicate the algorithm's ability to accurately classify DR severity levels, highlighting its potential for early detection and prevention of diabetes-related blindness.

关键词： Tunicate Swarm algorithm Renyi's entropy butterfly optimization algorithm Dice coefficient

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A Novel Hybrid algorithm Based on butterfly and Flower Pollination algorithms for Scheduling Independent Tasks on Cloud Computing

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INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS 2025年第1期16卷 842-850页

作者： Shao, Huiying Hebei Vocat Univ Technol & Engn Xingtai 054000 Peoples R China

Could computing is an Internet-based computing paradigm where virtual servers or workstations are offered as platforms, software, infrastructure, and resources. Task scheduling is considered one of the major NP-hard problems in cloud environments, posing several challenges to efficient resource allocation. Many metaheuristic algorithms have been extensively employed to address these task-scheduling problems as discrete optimization problems and have given rise to some proposals. However, these algorithms have inherent limitations due to local optima and convergence to poor results. This paper suggests a hybrid strategy for organizing independent tasks in heterogeneous cloud resources by incorporating the butterfly optimization algorithm (BOA) and Flower Pollination algorithm (FPA). Although BOA suffers from local optima and loss of diversity, which may cause an early convergence of the swarm, our hybrid approach outperforms such weaknesses by exploiting a mutualism-based mechanism. Indeed, the proposed hybrid algorithm outperforms existing methods while considering different task quantities with better scalability. Experiments are conducted within the CloudSim simulation framework with many task instances. Statistical analysis is performed to test the significance of the obtained results, which confirms that the suggested algorithm is effective at solving cloud-based task scheduling issues. The study findings indicate that the hybrid metaheuristic algorithm could be a promising approach to improving resource utilization and optimizing cloud task scheduling.

关键词： Task scheduling cloud computing butterfly optimization algorithm flower pollination algorithm mutualism

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

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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

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

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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

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Chaos-Enhanced Adaptive Hybrid butterfly Particle Swarm optimization algorithm for Passive Target Localization

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SENSORS 2022年第15期22卷 5739-5739页

作者： Rosic, Maja Sedak, Milos Simic, Mirjana Pejovic, Predrag Univ Belgrade Fac Mech Engn Belgrade 11000 Serbia Univ Belgrade Sch Elect Engn Belgrade 11000 Serbia

This paper considers the problem of finding the position of a passive target using noisy time difference of arrival (TDOA) measurements, obtained from multiple transmitters and a single receiver. The maximum likelihood (ML) estimator's objective function is extremely nonlinear and non-convex, making it impossible to use traditional optimization techniques. In this regard, this paper proposes the chaos-enhanced adaptive hybrid butterfly particle swarm optimization algorithm, named CAHBPSO, as the hybridization of butterfly optimization (BOA) and particle swarm optimization (PSO) algorithms, to estimate passive target position. In the proposed algorithm, an adaptive strategy is employed to update the sensory fragrance of BOA algorithm, and chaos theory is incorporated into the inertia weight of PSO algorithm. Furthermore, an adaptive switch probability is employed to combine global and local search phases of BOA with the PSO algorithm. Additionally, the semidefinite programming is employed to convert the considered problem into a convex one. The statistical comparison on CEC2014 benchmark problems shows that the proposed algorithm provides a better performance compared to well-known algorithms. The CAHBPSO method surpasses the BOA, PSO and semidefinite programming (SDP) algorithms for a broad spectrum of noise, according to simulation findings, and achieves the Cramer-Rao lower bound (CRLB).

关键词： localization time difference of arrival butterfly optimization algorithm hybrid optimization particle swarm optimization Cramer-Rao lower bound

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Effective privacy preserving model based on adversarial CNN with IBOA in the social IoT systems for CEC

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INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS 2025年第1期38卷

作者： Shukla, Prashant Kumar Pandit, Shraddha V. Gandhi, Charu Alrizq, Mesfer Alghamdi, Abdullah Shukla, Piyush Kumar Venkatareddy, Prashanth Rizwan, Ali Koneru Lakshmaiah Educ Fdn Dept Comp Sci & Engn Guntur Andhra Pradesh India PES Modern Coll Engn Dept Artificial Intelligence & Data Sci Pune Maharashtra India Jaypee Inst Informat Technol Dept CSE IT Noida Uttar Pradesh India Najran Univ Coll Comp Sci & Informat Syst Informat Syst Dept Najran Saudi Arabia Rajiv Gandhi Proudyogiki Vishwavidyalaya Univ Inst Technol Dept Comp Sci & Engn Technol Univ Madhya Pradesh Bhopal 462033 Madhya Pradesh India NITTE Meenakshi Inst Technol Dept Elect & Elect Bangalore Karnataka India King Abdulaziz Univ Fac Engn Dept Ind Engn Jeddah Saudi Arabia

In the present scenario, the social Internet of Things (IoT) is one of the emerging technologies in combination with collaborative edge computing (CEC). The CEC solves the issue of storage and computing with the help of deep learning models that make full usage of the edge-computing abilities. The robustness of the deep learning models is ineffective, and the edge devices in the CEC are threatened by the malicious attacks. Therefore, a new data protection framework is proposed in this manuscript to avoid the security crisis and privacy leakage of CEC. A new adversarial sample generation model is introduced in this manuscript on the basis of intensive butterfly optimization algorithm (IBOA) that effectively reduces the time complexity of the framework to linear. Here, IBOA is chosen because an extra intensive exploitation step is included that guides the proposed framework to converge quickly towards global optimum and to avoid local optima trap. Then, the adversarial training concept is incorporated with the convolutional neural network (CNN) model for sentence similarity analysis. On the other hand, the anti-dropout layers are combined with the adversarial CNN model to reduce the overfitting concern. The proposed framework, adversarial CNN with IBOA, obtained higher results with f1-score of 88.70%, accuracy of 89.40%, correct rate of 88.95%, recall of 89.48%, and precision of 89.41% on the Microsoft Research Paraphrase Corpus (MSRP) dataset. The obtained results are superiorly better than the conventional deep learning models. This research introduced an adversarial sample generation model using the proposed intensive butterfly optimization algorithm (IBOA) that effectively reduces the time complexity of the framework. The adversarial data samples are created by implementing IBOA and the implementation of IBOA results in effective generation text with less linear time ***

关键词： adversarial samples butterfly optimization algorithm collaborative edge computing convolutional neural network Internet of Things privacy leakage security crises

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ANFIS-optimized control for resilient and efficient supply chain performance in smart manufacturing

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RESULTS IN ENGINEERING 2025年 25卷

作者： Abouelaz, Mona A. Alhasnawi, Bilal Naji Sedhom, Bishoy E. Bures, Vladimir Mansoura Univ Fac Engn Prod Engn & Mech Design Dept Mansoura 35516 Egypt Imam Jaafar Al Sadiq Univ Coll Informat Technol Dept Comp Tech Engn Al Muthanna 66002 Iraq Mansoura Univ Fac Engn Elect Engn Dept Mansoura 35516 Egypt Univ Hradec Kralove Fac Informat & Management Hradec Kralove 50003 Czech Republic

Due to the dramatic revolution in global trade, competition, and the epidemic of COVID-19, the Small and Medium Enterprises (SME's) production paradigm has been evolving and gaining traction to meet its dynamic demands and challenges for industrial process adaptability and standards. As a result, they develop CyberPhysical Production Systems (CPPS) by integrating CPS modules into their manufacturing processes. This integration is founded on the belief that value-added services result from technological advancements. Better tools would be needed in the future to provide process management, monitoring, and maintenance. Our main goal is to support existing SMEs with an economically adaptable solution for technological improvement. So, to make the proposed solution sustainable, the whole process must be analyzed, from the input of raw materials to the output of finished products. This paper evaluates the supply chain (SC) using the adaptive neuro-fuzzy inference system (ANFIS) classification control algorithm to improve the SC performance, maximize the system quality, and minimize the cost. Also, the butterfly optimization algorithm (BOA) is proposed for obtaining optimal parameters for the ANFIS controller algorithm. The performance of the SC is evaluated on the real-time production system, and the results are analyzed to prove the effectiveness of the proposed algorithm. The proposed algorithm can be applied to CPS components in the current SME environment to improve the performance of manufacturing processes.

关键词： Smart manufacturing Cyber-physical systems Operational cost Operation control Adaptive neuro-fuzzy inference system butterfly optimization algorithm

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Early diagnosis of liver disease using improved binary butterfly optimization and machine learning algorithms

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MULTIMEDIA TOOLS AND APPLICATIONS 2024年第10期83卷 30867-30895页

作者： Shaban, Warda M. Nile Higher Inst Engn & Technol Mansoura Egypt

Liver disease in patients is on the rise due to environmental factors like toxic gas exposure, contaminated food, drug interactions, and excessive alcohol use. Therefore, diagnosing liver disease is crucial for saving lives and managing the condition effectively. In this paper, a new method called Liver Patients Detection Strategy (LPDS) is proposed for diagnosing liver disease in patients from laboratory data alone. The three main parts of LPDS are data preprocessing, feature selection, and detection. The data from the patient is processed, and any anomalies are removed during this stage. Then, during feature selection phase, the most helpful features are chosen. A novel method is proposed to choose the most relevant features during the feature selection stage. The formal name for this method is IB(2)OA, which stands for Improved Binary butterfly optimization algorithm. There are two steps to IB(2)OA, which are;Primary Selection (PS) step and Final Selection (FS) step. This paper presents two enhancements. The first is Information Gain (IG) approach, which is used for initial feature reduction. The second is implementing BOA's initialization with optimization Based on Opposition (OBO). Finally, five different classifiers, which are Support Vector Machine (SVM), K-Nearest Neighbor (KNN), Naive Bayes (NB), Decision Tree (DT), and Random Forest (RF) are used to identify patients with liver disease during the detection phase. Results from a battery of experiments show that the proposed IB(2)OA outperforms the state-of-the-art methods in terms of precision, accuracy, recall, and F-score. In addition, when compared to the state-of-the-art, the proposed model's average selected features score is 4.425. In addition, among all classifiers considered, KNN classifier achieved the highest classification accuracy on the test dataset.

关键词： Liver Disease Feature selection butterfly optimization algorithm Classification

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Prediction optimization of Cervical Hyperextension Injury: Kernel Extreme Learning Machines With Orthogonal Learning butterfly Optimizer and Broyden- Fletcher-Goldfarb-Shanno algorithms

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IEEE ACCESS 2020年 8卷 119911-119930页

作者： Liu, Guomin Jia, Wenyuan Luo, Yungang Wang, Mingjing Heidari, Ali Asghar Ouyang, Jinsheng Chen, Huiling Chen, Mayun Second Hosp Jilin Univ Dept Orthoped Changchun 130041 Peoples R China Second Hosp Jilin Univ Dept Stomatol Changchun 130041 Peoples R China Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam Univ Tehran Coll Engn Sch Surveying & Geospatial Engn Tehran *** Iran Natl Univ Singapore Sch Comp Dept Comp Sci Singapore 117417 Singapore Wenzhou Med Univ Affiliated Hosp 1 Dept Pulm & Crit Care Med Wenzhou 325000 Peoples R China Wenzhou Univ Coll Comp Sci & Artificial Intelligence Wenzhou 325035 Peoples R China

In this research, X-ray and MRI images of patients suffering from cervical hyperextension injury are investigated. Also, radiographic images are collected from patients who suffered from trauma but without cervical hyperextension injury. The core engine algorithm of the optimized prediction model is kernel extreme learning machine (KELM), and the input data is 17 factors that may cause cervical hyperextension injury. As the optimization core, we utilized the butterfly optimization algorithm (BOA). Up to now, few improved variants of BOA have been reported. The original BOA converges slowly and quickly falls into a locally optimal solution. An enhanced BOA based on orthogonal learning, Levy flight, and an exploitation engine is proposed in this paper to relieve these two shortcomings, which is called LBOLBOA. Orthogonal learning is utilized to construct guidance vectors for guiding agents toward the global optimum solution aiming to increase the accuracy of the solutions. Also, Levy flight and Broyden-Fletcher-Goldfarb-Shanno mechanisms are utilized to enrich the intensification propensities of BOA and stagnation avoidance. The proposed LBOLBOA is used to deal with continuous function optimization and machine learning problems, including parameter optimization of KELM. We rigorously verified this variant using a comprehensive set of the benchmark test suite and real-world dataset on cervical hyperextension injury. The results indicate that LBOLBOA can achieve improved performance in dealing with the function optimization and machine learning problems, especially the capability for prediction of cervical hyperextension injury.

关键词： optimization Prediction algorithms Predictive models Injuries Kernel Support vector machines Benchmark testing butterfly optimization algorithm medical diagnosis kernel extreme learning machine parameter optimization global optimization

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