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Binary butterfly optimization approaches for feature selection

EXPERT SYSTEMS WITH APPLICATIONS 2019年 116卷 147-160页

作者： Arora, Sankalap Anand, Priyanka DAV Univ Jalandhar Punjab India Lovely Profess Univ Jalandhar Punjab India

In this paper, binary variants of the butterfly optimization algorithm (BOA) are proposed and used to select the optimal feature subset for classification purposes in a wrapper-mode. BOA is a recently proposed algorithm that has not been systematically applied to feature selection problems yet. BOA can efficiently explore the feature space for optimal or near-optimal feature subset minimizing a given fitness function. The two proposed binary variants of BOA are applied to select the optimal feature combination that maximizes classification accuracy while minimizing the number of selected features. In these variants, the native BOA is utilized while its continuous steps are bounded in a threshold using a suitable threshold function after squashing them. The proposed binary algorithms are compared with five state-of-the-art approaches and four latest high performing optimization algorithms. A number of assessment indicators are utilized to properly assess and compare the performance of these algorithms over 21 datasets from the UCI repository. The experimental results confirm the efficiency of the proposed approaches in improving the classification accuracy compared to other wrapper-based algorithms, which proves the ability of BOA algorithm in searching the feature space and selecting the most informative attributes for classification tasks. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Binary butterfly optimization algorithm butterfly optimization algorithm Feature selection Bio-inspired optimization

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Fatigue life prediction of welded joints based on multi-strategy improved butterfly algorithm 25

Fatigue life prediction of welded joints based on multi-stra...

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Proceedings of the 4th International Conference on Computer, Artificial Intelligence and Control Engineering

作者： Xinyu Cao School of Railway Intelligent Engineering Dalian Jiaotong University Dalian Liaoning China

ISBN: (纸本)9798400712647

Currently, machine learning has been developed as an effective method for fatigue life prediction, but the accuracy on fatigue life prediction of welded joints under complex loads is still low. To solve this problem, this work proposes a butterfly optimization algorithm (BOA) based on multiple strategy improvement combined with kernel extreme learning machine (KELM) model for predicting the residual fatigue life of welded joints under two-level loading. Firstly, Circle chaotic mapping is is applied to improve the diversity of the initial population. And the dynamic switch probability and adaptive weight factor are introduced to effectively balance the global and local searches to avoid falling into the local optimum. Then the parameters of its optimized KELM are used to build the fatigue life prediction model of the improved BOA optimized KELM and compare it with other algorithm optimization models. The experimental results show that the prediction effect of the suggested model is better than other models and the prediction accuracy is high.

关键词： butterfly optimization algorithm

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A hybrid intelligent predicting model for exploring household CO2 emissions mitigation strategies derived from butter fly optimization algorithm

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SCIENCE OF THE TOTAL ENVIRONMENT 2020年 727卷 138572-138572页

作者： Wen, Lei Cao, Yang North China Elect Power Univ Dept Econ & Management Baoding 071003 Hebei Peoples R China

The warming on earth is having a profound impact on human survival. With the improvement of people' living standard, the consumption of energy in residential sector has raised swiftly, leading to a rapid increase in corresponding CO2 emissions. To effectively mitigate household emissions, taking the Yangtze River Delta (YRD) region in China as a case study, this paper proposes a novel intelligent model combining driving forces exploration and prediction. The work first estimates the residential energy-related CO2 emissions precisely, and then the bivariate correlation analysiswill be applied to analyze region discrepancy inmain affecting factors of emissions based on 13 preliminary indicators. To obtain the principal information of above influencing factors as the input of prediction model, the kernel principal component analysis (KPCA) is introduced innovatively. Besides, butterfly optimization algorithm (BOA) is enhanced to better optimize the parameters of least square support vector machine (LSSVM). The newproposed hybridmodel, hereafter called as EBOA-LSSVM, will be utilized to predict residential CO2 emissions in the YRD. Ultimate simulation results present the new model's prominent performance through comparing prediction accuracywith othermodels. Finally, this article provides someadvice for policy makers to guide CO2 emissions reduction from residents department. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Household CO2 emissions Bivariate correlation analysis butterfly optimization algorithm Least square support vector machine

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Eye-Tracking Based Autism Spectrum Disorder Diagnosis Using Chaotic butterfly optimization with Deep Learning Model

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Computers, Materials & Continua 2023年第8期76卷 1995-2013页

作者： Tamilvizhi Thanarajan Youseef Alotaibi Surendran Rajendran Krishnaraj Nagappan Department of Computer Science and Engineering Panimalar Engineering CollegeChennai600123India Department of Computer Science University College of Al JamoumUmm Al-Qura UniversityMakkah21421Saudi Arabia Department of Computer Science and Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India School of Computing SRM Institute of Science and TechnologyKattankulathur603203India

Autism spectrum disorder(ASD)can be defined as a neurodevelopmental condition or illness that can disturb kids who have heterogeneous characteristics,like changes in behavior,social disabilities,and difficulty communicating with *** tracking(ET)has become a useful method to detect *** vital aspect of moral erudition is the aptitude to have common visual *** eye-tracking approach offers valuable data regarding the visual behavior of children for accurate and early ***-tracking data can offer insightful information about the behavior and thought processes of people with ASD,but it is important to be aware of its limitations and to combine it with other types of data and assessment techniques to increase the precision of ASD *** operates by scanning the paths of eyes for extracting a series of eye projection points on images for examining the behavior of children with *** purpose of this research is to use deep learning to identify autistic disorders based on eye *** Chaotic butterfly optimization technique is used to identify this specific ***,this study develops an ET-based Autism Spectrum Disorder Diagnosis using Chaotic butterfly optimization with Deep Learning(ETASD-CBODL)*** presented ETASDCBODL technique mainly focuses on the recognition of ASD via the ET and DL *** accomplish this,the ETASD-CBODL technique exploits the U-Net segmentation technique to recognize interested *** addition,the ETASD-CBODL technique employs Inception v3 feature extraction with CBO algorithm-based hyperparameter ***,the long-shorttermmemory(LSTM)model is exploited for the recognition and classification of *** assess the performance of the ETASD-CBODL technique,a series of simulations were performed on datasets from the figure-shared data *** experimental values of accuracy(99.29%),precision(98.78%),sensitivity(99.29%)and specificity(99.29%)showed a better perfo

关键词： Eye tracking ASD diagnosis deep learning butterfly optimization algorithm behavior analysis

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Suspended sediment prediction using integrative soft computing models: on the analogy between the butterfly optimization and genetic algorithms

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GEOCARTO INTERNATIONAL 2022年第4期37卷 961-977页

作者： Fadaee, Marzieh Mahdavi-Meymand, Amin Zounemat-Kermani, Mohammad Shahid Bahonar Univ Kerman Water Engn Dept Kerman Iran

The present study investigates the capability of two metaheuristic optimization approaches, namely the butterfly optimization algorithm (BOA) and the Genetic algorithm (GA), integrated with machine learning models in Suspended Sediment Load (SSL) prediction. The Adaptive Neuro-Fuzzy Inference System (ANFIS), Artificial Neural Network (ANN), and Multiple Linear Regression (MLR) are applied as the predictive data-driven models. Independent input variables, i.e., the water temperature (T), river discharge (Q), and specific conductance (SC) are used for the prediction ofSSLbased on several statistical indices. The results indicate that the performances of all studied models were close to one another;moreover, the metaheuristic algorithms were found to increase the accuracy of the ANFIS and ANN models for approximately 11.73 percent and 4.30 percent, respectively. In general, the BOA outperformed the GA in enhancing the optimization performance of the learning process in the applied machine learning models.

关键词： Data mining butterfly optimization algorithm neural computing suspended sediment load

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Investigation of butterfly optimization and gases Brownian motion optimization algorithms for optimal multilevel image thresholding

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 182卷 115286-115286页

作者： Sowjanya, Kotte Injeti, Satish Kumar Kakatiya Inst Technol & Sci A Dept Elect & Commun Engn Warangal 506015 Telangana India Natl Inst Technol Warangal Dept Elect Engn Warangal 506004 Telangana India

Image processing is one of the major research regions in the most recent decades, in which, image segmentation has gained more popularity due to its enormous applications. Before segmenting an image, it has to be preprocessed using thresholding techniques. A properly thresholded image would give better segmentation results. Many researchers have contributed significant algorithms based on Otsu and Kapur based methods and reported outstanding results. These methods are suitable for bi-level as well as multilevel thresholding cases, however, the process of selecting the optimal thresholds in the case of multilevel thresholding is difficult and time consuming. To avoid this problem, this paper investigates the ability of two different algorithms namely: BOA (butterfly optimization algorithm) and GBMO (Gases Brownian Motion optimization) to determine the optimal threshold levels for image segmentation. The BOA is inspired from natural behaviour of butterflies which have special style of information propagation based on intensity of fragrance;whereas, GBMO is based on law of motion of gases which have greater ability to trade-off between exploration and exploitation. The candidate solutions were created using image histogram based on selected algorithms, and then they were updated according to the characteristics of each algorithm. The solutions are estimated using Otsu's between class variance as fitness function during the process of optimization. The performance of the proposed algorithms has been evaluated using various benchmark images and has been compared with seven different algorithms such as MFO (Moth Flame optimization), WOA (Whale optimization algorithm), SSO (Social Spider optimization), FASSO (Firefly Assisted Social Spider optimization), FA (Firefly algorithm), SCA (Sine Cosine algorithm), and HS (Harmony Search). The outcomes have been analysed based on various metrics like fitness value, PSNR and SSIM, as well as time complexity and the Wilcoxon statisti

关键词： Multilevel thresholding Grayscale images butterfly optimization algorithm Gases Brownian Motion optimization Qualitative and quantitative analysis

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A Hybrid Moth Flame optimization algorithm for Global optimization

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Journal of Bionic Engineering 2022年第5期19卷 1522-1543页

作者： Saroj Kumar Sahoo Apu Kumar Saha Department of Mathematics National Institute of TechnologyAgartalaTripura 799046India

The Moth Flame optimization(MFO)algorithm shows decent performance results compared to other meta-heuristic algorithms for tackling non-linear constrained global optimization ***,it still suffers from obtaining quality solution and slow convergence *** the other hand,the butterfly optimization algorithm(BOA)is a comparatively new algorithm which is gaining its popularity due to its simplicity,but it also suffers from poor exploitation *** this study,a novel hybrid algorithm,h-MFOBOA,is introduced,which integrates BOA with the MFO algorithm to overcome the shortcomings of both the algorithms and at the same time inherit their *** performance evaluation,the proposed h-MFOBOA algorithm is applied on 23 classical benchmark functions with varied *** tested results of the proposed algorithm are compared with some well-known traditional meta-heuristic algorithms as well as MFO *** rank test and Wilcoxon signed rank test are employed to measure the performance of the newly introduced algorithm *** computational complexity has been ***,the proposed algorithm has been applied to solve one constrained and one unconstrained real-life problems to examine its problem-solving capability of both type of *** comparison results of benchmark functions,statistical analysis,real-world problems confirm that the proposed h-MFOBOA algorithm provides superior results compared to the other conventional optimization algorithms.

关键词： Moth fame optimization algorithm butterfly optimization algorithm Bio-inspired Benchmark functions Friedman rank test

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

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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

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Data fusion algorithm of wireless sensor network based on clustering and fuzzy logic

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TELECOMMUNICATION SYSTEMS 2024年第4期86卷 617-626页

作者： Yu, Xiuwu Peng, Wei Zhang, Ke Zhou, Zixiang Liu, Yong Univ South China Sch Resource Environm & Safety Engn Hengyang 421001 Peoples R China Shenzhen Univ Coll Phys & Optoelect Engn Shenzhen 518000 Peoples R China

In order to reduce network energy consumption and prolong the network lifetime in wireless sensor networks, a data fusion algorithm named CFLDF is proposed. Firstly, upon completion of the arrangement of network nodes, network clustering is achieved using fuzzy c-means optimized by the improved butterfly optimization algorithm, and a data fusion model is established on the clustering structure. Then, reliable data is sent to the cluster head by the nodes with the assistance of a fuzzy logic controller, and data fusion is performed by the cluster head using a fuzzy logic algorithm. Finally, cluster heads transmit the fused data to the base station. Finally, the fused data is transmitted to the base station by the cluster heads. Simulation experiments are conducted to evaluate the CFLDF algorithm against the LEACH, LEACH-C, and SEECP algorithms. The results demonstrate that network energy consumption is effectively reduced and the network lifetime is extended by the CFLDF algorithm.

关键词： Wireless sensor network Data fusion Fuzzy c-means butterfly optimization algorithm Fuzzy logic

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

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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

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