Butterfly Optimization Algorithm Research Articles

Copper Price Forecasting Based on Improved Least Squares Support Vector Machine with Butterfly Optimization Algorithm

Copper Price Forecasting Based on Improved Least Squares Support Vector Machine with Butterfly Optimization Algorithm

Rapid Detection of Endogenous Impurities in Walnuts Using Near-Infrared Hyperspectral Imaging Technology

Impurities in walnut-based products pose a significant risk to human health, necessitating the development of rapid and efficient methods for detecting endogenous impurities in walnut shell-breaking materials. To address this issue, this study aims to investigate the feasibility of near-infrared hyperspectral imaging (NIR-HSI) in conjunction with conventional chemometric methods and deep learning models. First, biochemical experiments demonstrated differences in the total phenolic and flavonoid contents of different walnut kernels. An improved support vector machine model based on the butterfly optimization algorithm was constructed for classification using an NIR dataset of endogenous impurities in walnuts, achieving an accuracy of 96.12%. In addition, this study proposed the WT-NIRSNet model, a deep neural network with an additional attention mechanism, and compared it to a traditional chemometric approach. The study demonstrated an accuracy of 99.03% on the test set using WT-NIRSNet to solve the fast classification problem of endogenous impurities in walnuts. It can achieve desirable classification results using only raw spectral data without complex preprocessing operations and is superior to traditional chemometric models. Therefore, the NIR-HSI technique combined with deep neural networks has a significant potential for detecting endogenous impurities in walnuts.

Short-term wind power prediction using a novel model based on butterfly optimization algorithm-variational mode decomposition-long short-term memory

The precise forecasting of wind power output is crucial for the integration of large-scale renewable energy generation into the power grid. It plays a vital role in ensuring safe and stable operation of power grid, diminishing fuel consumption, and minimizing environmental impacts. This research presents a machine learning prediction model that integrates intelligent optimization algorithms and data decomposition techniques for short-term wind power output forecasting. The initial phase of this research develops a prediction model utilizing variational mode decomposition and long short-term memory networks. To mitigate the randomness and uncertainty of wind energy, and improve prediction accuracy, the butterfly optimization algorithm is introduced to optimize the parameters of variational mode decomposition and long short-term memory networks. Several in-depth case studies are carried out on the actual wind power generation dataset in mainland China to confirm the feasibility and effectiveness of the proposed hybrid model. Experimental results demonstrate that the proposed model outperforms the compared model in terms of prediction accuracy across different seasons, showing good practicality and generalizability.

Power quality improvement in grid-connected photovoltaic system under partial shading condition

ABSTRACT Solar systems are becoming widely used renewable energy source since photovoltaic cell generates high power output. However, during partial shading conditions (PSC), the existing MPPT techniques failed to determine the actual maximum point of the photovoltaic (PV) cell, and the photovoltaic system is stuck at a local maximum thus output is gradually reduced. The Optimized Grid-Connected Photovoltaic system aims to extract the most power from the solar array under partial shading conditions. It uses an optimisation algorithm named Sovereign Butterfly Optimization Algorithm and a sequence controller to optimize power value selection. The voltage harmonics and phase delay cause the system to be in an unbalanced state, so the proposed model uses an anomaly obliteration filter fusion technique in which the Butterworth filter is utilised for reducing voltage harmonics and removing phase delay completely by avoiding negative sequence by converting three-phase current to dq0 frames. To increase the overall power quality in the photovoltaic system without synchronization problems, a Subsystem Synchronization Control Strategy is applied which effectively synchronizes the active and non-active currents injected into the grid. Simulated results using the proposed model showed high tracking accuracy of 99.23% and minimum power oscillation of 2.53%, thus enhancing the overall power quality.

A Comparison of Inversion Methods for Surrogate‐Based Groundwater Contamination Source Identification With Varying Degrees of Model Complexity

AbstractAccurate identification of groundwater contamination sources is important for designing efficacious site remediation strategies. Currently, the methods for identifying contamination sources mainly fall into three distinct categories: simulation optimization, Bayesian inference, and data assimilation. Each method has its own advantages and disadvantages under specific site conditions. To evaluate the applicability of these methods, we chose one representative inversion algorithm from each category, namely the Improved Butterfly Optimization Algorithm (IBOA) for simulation optimization, the Ensemble Smoother with Multiple Data Assimilation (ES‐MDA) for data assimilation, and the DiffeRential Evolution Adaptive Metropolis with a Snooker Update and Sampling from a Past Archive (DREAM(ZS)) for Bayesian inference. We conducted a comprehensive evaluation of these methods' performance under different model complexities, employing a surrogate model as a substitute for the complex forward model. By addressing two distinct problems involving conservative pollutant transport and Light Non‐Aqueous Phase Liquid (LNAPL) transport with biodegradation, we employed four criteria (elapsed time, result accuracy, posterior probability distribution, and noise resistance) for evaluation. The findings unequivocally indicate that DREAM(ZS) outperforms others in terms of result accuracy and posterior probability distribution. It also adeptly navigates the interrelations among disparate unknown variables. The strength of ES‐MDA lies in its efficiency. It achieves relatively satisfactory results with a reduced computational burden. In contrast, IBOA underperforms in both test problems. In terms of resistance to noise, both DREAM(ZS) and ES‐MDA perform better than IBOA does.

Prediction of RNA Secondary Structure Using Butterfly Optimization Algorithm

AbstractRibonucleic acid (RNA) structure is vital to its ability to function within the cell. The ability to predict RNA structure is essential to implementing new medications and understanding genetic illnesses. It is also important in synthetic and computational biology. All these functions are directly related to its secondary structure. Also prediction of RNA secondary structure process is the most significant step to determining the tertiary structure of RNA. On account of this, prediction of secondary structure of RNA is the crying topic in bioinformatics. In this research, we present the swarm-based metaheuristic Butterfly Optimization Algorithm (BOA) method for predicting the secondary structure of RNA. The main feather of the BOA is that it can conduct both local and global search simultaneously. According to the problem perspective, we have redesigned the operators of BOA to perform global and local search operations in different ways. We have followed a thermodynamic model for the selection of the stable secondary structure with minimum Gibbs free energy. Predicting the minimum free energy value we also developed an “Optimize” function to search the new optimize structure. This function increases the prediction efficiency, creating new stable structure and also decreases the time complexity of global searching procedure. We have used a public dataset to perform the prediction operation. To accuse our prediction efficiency, we have compared our outcomes to existing popular algorithms. The result shows that the proposed approach can predict secondary RNA structure better than other state-of-the-art algorithms.

Feature Selection Techniques for Enhancing Credit Card Fraud Detection Performance: A Hybrid Metaheuristic Approach Using Nature-Inspired Algorithms

Abstract: The surge in credit card usage has led to a parallel increase in fraudulent transactions, necessitating advanced detection systems. Traditional methods encounter challenges with high-dimensional and imbalanced datasets. This paper proposes a comprehensive approach integrating metaheuristic algorithms and deep learning techniques to enhance fraud detection accuracy. The first contribution, the Rock Hyrax Swarm Optimization Feature Selection (RHSOFS) algorithm, draws inspiration from the collective behaviors of rock hyrax swarms to effectively select relevant features from high-dimensional datasets. RHSOFS identifies an optimal subset of features critical for fraud detection through supervised machine learning. Complementing this, the second contribution leverages a hybrid deep learning model. Beginning by organizing transactional data and constructing a Logical Graph of Behavior Profile (LGBP) to abstract transaction details, the Modified Butterfly Optimization Algorithm (MBOA) selects important features from the dataset. The hybrid model, integrating Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN), establishes rational connections between transactional characteristics, enhancing detection performance. To evaluate the approach, comparative efficiency analyses against existing methods, including Differential Evolutionary Feature Selection (DEFS), Genetic Algorithm Feature Selection (GAFS), Particle Swarm Optimization Feature Selection (PSOFS), and Ant Colony Optimization Feature Selection (ACOFS), are conducted. Results demonstrate the superiority of the approach in terms of both reliability and recognition rate, validated through rigorous statistical testing. The proposed hybrid approach signifies a significant advancement in credit card fraud detection systems, offering enhanced accuracy and efficiency in combating fraudulent activities amidst the growing complexity of transactional datasets.

Structural damage identification with output-only strain measurements and swarm intelligence algorithms: a comparative study

The identification of structural damage with the unavailability of input excitations is highly desired but challenging since structural dynamic responses are affected by the coupling effect of structural parameters and external excitations. To deal with this issue, in this paper, an output-only damage identification strategy based on swarm intelligence algorithms and correlation functions of strain responses is proposed to identify structures subjected to single or multiple unknown white noise excitations. In the proposed strategy, four different population-based optimization algorithms—particle swarm optimization, the butterfly optimization algorithm, the tree seed algorithm, and a micro search Jaya (MS-Jaya)—are employed and compared. The micro search mechanism is integrated into a basic Jaya algorithm to improve its computational efficiency and accuracy by eliminating some damage variables with small values for the identified best solution after several iterations. The objective function is established based on the proposed auto/cross-correlation function of strain responses and a penalty function. The effectiveness of the proposed method is verified with numerical studies on a simply supported beam structure and a steel grid benchmark structure under ambient excitation. In addition, the effect of the reference point, number of sensors, and arrangement of strain gauges on the performance of the proposed method are discussed in detail. The investigated results demonstrate that the proposed approach can accurately detect, locate, and quantify structural damage with limited sensors and 20% noise-polluted strain responses. In particular, the proposed MS-Jaya algorithm presents a more superior capacity in solving the optimization-based damage identification problem than the other three algorithms.

Modified Archimedes optimization algorithm for global optimization problems: a comparative study

Archimedes Optimization Algorithm (AOA) is a recent optimization algorithm inspired by Archimedes’ Principle. In this study, a Modified Archimedes Optimization Algorithm (MDAOA) is proposed. The goal of the modification is to avoid early convergence and improve balance between exploration and exploitation. Modification is implemented by a two phase mechanism: optimizing the candidate positions of objects using the dimension learning-based (DL) strategy and recalculating predetermined five parameters used in the original AOA. DL strategy along with problem specific parameters lead to improvements in the balance between exploration and exploitation. The performance of the proposed MDAOA algorithm is tested on 13 standard benchmark functions, 29 CEC 2017 benchmark functions, optimal placement of electric vehicle charging stations (EVCSs) on the IEEE-33 distribution system, and five real-life engineering problems. In addition, results of the proposed modified algorithm are compared with modern and competitive algorithms such as Honey Badger Algorithm, Sine Cosine Algorithm, Butterfly Optimization Algorithm, Particle Swarm Optimization Butterfly Optimization Algorithm, Golden Jackal Optimization, Whale Optimization Algorithm, Ant Lion Optimizer, Salp Swarm Algorithm, and Atomic Orbital Search. Experimental results suggest that MDAOA outperforms other algorithms in the majority of the cases with consistently low standard deviation values. MDAOA returned best results in all of 13 standard benchmarks, 26 of 29 CEC 2017 benchmarks (89.65%), optimal placement of EVCSs problem and all of five real-life engineering problems. Overall success rate is 45 out of 48 problems (93.75%). Results are statistically analyzed by Friedman test with Wilcoxon rank-sum as post hoc test for pairwise comparisons.

Speaker recognition using Improved Butterfly Optimization Algorithm with hybrid Long Short Term Memory network

Speaker recognition using Improved Butterfly Optimization Algorithm with hybrid Long Short Term Memory network

Path planning in three-dimensional space based on butterfly optimization algorithm

Path planning is one of the most critical issues in many related fields including UAVs. Many researchers have addressed this problem according to different conditions and limitations, but modelling the 3-D space and routing with an evolutional algorithm in such spaces is an open issue. So, in this paper, we first, introduce a method to grids the environment using geometrical shapes. This can reduce the random states of cell decomposition and increases the computational speed. We then propose an effective routing algorithm based on the butterfly optimization algorithm (BOA). It can simultaneously optimize multiple path planning objectives. It uses an objective function to compute the shortest path, based on obstacle avoidance and the UAV’s operational power minimization. A novel concept, the intelligent throwing agent, used in this algorithm prevents getting stuck in local optima and increases the network coverage in path planning. The throwing agent prevents the collision of the UAV with the obstacles using geometrical techniques and contour lines. The simulation results show that BOA has the least and second-least cost in best-case and worst-case scenarios in comparison with ant colony and particle swarm. Its run time and the optimal value of the fitting function are also better than the two other algorithms.

Thresholding Chaotic Butterfly Optimization Algorithm with Gaussian Kernel (TCBOGK) based segmentation and DeTrac deep convolutional neural network for COVID-19 X-ray images

Thresholding Chaotic Butterfly Optimization Algorithm with Gaussian Kernel (TCBOGK) based segmentation and DeTrac deep convolutional neural network for COVID-19 X-ray images

Optimizing processing parameters in electrical discharge milling of CFRP using metaheuristics

ABSTRACT The micro-channel fabrication in carbon fiber-reinforced polymer (CFRP) composite through electrical discharge machining (EDM) process followed by the comprehensive study of dimensional deviation in the conceived micro-channels are presented in this work. The factors namely, voltage (V), tool rotational speed (TRS), and feed rate (FR) were considered for assessing their impact on channel width deviation (CWD). In addition to the variance analysis,, two potential metaheuristic optimizers, the Butterfly optimization algorithm (BOA) and the Spotted Hyena optimization algorithm (SHO) were used to identify the optimal operating parameters of the aforementioned process. The BOA revealed that the optimal process variables were FR = 5.99 µm/s, V = 150 volt, and TRS = 577.08 rpm for a CWD = 74.96 µm. In contrary, SHO revealed that FR = 5.99 µm/s, V = 150 volt, TRS = 542.96 rpm, and CWD = 74.53 µm were the corresponding optimal values. Overcuts were observed in the fabricated micro-channels and a detailed illustration of the mechanism of overcut formation was provided.

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

SummaryIn 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.

A new hybrid Lévy Quantum-behavior Butterfly Optimization Algorithm and its application in NL5 Muskingum model

<abstract><p>This paper presents a novel hybrid algorithm that combines the Butterfly Optimization Algorithm (BOA) and Quantum-behavior Particle Swarm Optimization (QPSO) algorithms, leveraging $ gbest $ to establish an algorithm communication channel for cooperation. Initially, the population is split into two equal subgroups optimized by BOA and QPSO respectively, with the latter incorporating the Lévy flight for enhanced performance. Subsequently, a hybrid mechanism comprising a weight hybrid mechanism, a elite strategy, and a diversification mechanism is introduced to blend the two algorithms. Experimental evaluation on 12 benchmark test functions and the Muskin model demonstrates that the synergy between BOA and QPSO significantly enhances algorithm performance. The hybrid mechanism further boosts algorithm performance, positioning the new algorithm as a high-performance method. In the Muskingum model experiment, the algorithm proposed in this article can give the best sum of the square of deviation (SSQ) and is superior in the comparison of other indicators. Overall, through benchmark test function experiments and Muskin model evaluations, it is evident that the algorithm proposed in this paper exhibits strong optimization capabilities and is effective in addressing practical problems.</p></abstract>

EABOA: Enhanced adaptive butterfly optimization algorithm for numerical optimization and engineering design problems

The butterfly optimization algorithm (BOA) is a meta-heuristic algorithm that mimics foraging and mating behavior of butterflies. In order to alleviate the problems of slow convergence, local optimum and lack of population diversity of BOA, an enhanced adaptive butterfly optimization algorithm (EABOA) is proposed in this paper. First, a new adaptive fragrance model is designed, which provided a finer fragrance perception way and effectively enhanced the convergence speed and accuracy. Second, Lévy flight with high-frequency short-step jumping and low-frequency long-step walking is adopted to help the algorithm jump out of the local optimum. Third, the dimension learning-based hunting is employed to enhance information exchange by creating neighbors for each butterfly, thus improving the balance between local and global search and maintaining population diversity. In addition, the Fitness-Distance-Constraint (FDC) method is introduced to enhance constraint handling in EABOA (named FDC-EABOA). The proposed EABOA is compared with 8 well-known algorithms and 8 BOA variants in CEC 2022 test suite and the results were statistically analyzed using Friedman, Friedman aligned rank, Wilcoxon signed rank, Quade rank and multiple comparisons, analysis of variance (ANOVA) and range analysis. Finally, EABOA and FDC-EABOA are applied to seven engineering problems (parameter identification of photovoltaic module model, speed reducer design, tension/compression spring design, pressure vessel design, gear train design, welded beam design, SOPWM for 3-level inverters), and metrics such as Improvement Index (IF) and Mean Constraint Violation (MV) confirm that the proposed algorithms are satisfactory. Experimental results and statistical analysis show that the proposed algorithms outperform the comparison algorithms and demonstrate the strong potential for solving numerical optimization and engineering design problems.

SMGR-BS: Stacking Multiple Gated Recurrent Butterfly Search Model-Based Innovative AAL for Aging and Disabled Individuals

Ambient assisted living (AAL) for aging and disabled people involves creating supportive environments that leverage technology to improve the quality of life and independence of these individuals. Traditional methods for developing AAL solutions for aging and disabled people face several challenges, such as scalability, high costs, and privacy concerns. To tackle these complexities, this article proposed a novel method named stacking multiple gated recurrent-based butterfly search (SMGR-BS) for the development of AAL for aging and disabled people. In this study, stacking multiple gated recurrent units are utilized to capture intricate temporal dependencies in sensor data, and the deep recurrent neural network extracts the features from the variety of sensor inputs. Also, the butterfly optimization algorithm with a local search strategy is employed to fine-tune the parameters and enhance the effectiveness of the SMGR-BS method. In this work, the experiments are conducted on the Mobile HEALTH dataset, and the performance evaluation of the SMGR-BS method involves analyzing its effectiveness based on evaluation metrics, namely specificity, F1-score, recall, precision, and accuracy, and comparing its performance against existing methodologies to assess its effectiveness. The experimental results illustrate the effectiveness of the SMGR-BS method for developing AAL for aging and disabled people.

Prediction of EV Charging Behavior using BOA-based Deep Residual Attention Network

In smart city applications, electric vehicles (EVs) are rapidly gaining popularity due to their ability to help cut down on carbon emissions. Numerous environmental conditions, including terrain, traffic, driving style, temperature, and so on, affect the amount of energy an EV needs to operate. However, the burden on power grid infrastructure from widespread EV deployment is one of the biggest obstacles. Smart scheduling algorithms can be used to handle the rising public charging demand. Scheduling algorithms can be improved using data-driven tools and procedures to study EV charging behaviour. Predictions of behaviour, including temperature, departure time, and energy requirements, have been the focus of research on past charging data. Weather, traffic, and surrounding events are all factors that have been mostly ignored but which could improve representations and predictions. The DRA-Net, or Deep Residual Attention Network, was developed by the researchers and is used to recognize EV charging patterns. To minimize data loss, the Res-Attention component utilized tighter connections and smaller convolutional kernels (3 x 3). In addition, an Artificial Butterfly Optimisation Algorithm (BOA) model is used to fine-tune the DRA-Net's hyper-parameters. We highlight the significance of traffic and weather info for charging behaviour predictions, and the study's experimental forecasts show a considerable improvement over prior work on the same dataset. The future of electric vehicle (EV) research has been mapped out thanks to in-depth study, and as a result, EVs will soon significantly impact the auto industry.

A Hybrid Extreme Gradient Boosting and Long Short-Term Memory Algorithm for Cyber Threats Detection

The vast amounts of data, lack of scalability, and low detection rates of traditional intrusion detection technologies make it impossible to keep up with evolving and increasingly sophisticated cyber threats. Therefore, there is an urgent need to detect and stop cyber threats early. Deep Learning has greatly improved intrusion detection due to its ability to self-learn and extract highly accurate features. In this paper, a Hybrid XG Boosted and Long Short-Term Memory algorithm (HXGBLSTM) is proposed. A comparative analysis is conducted between the computational performance of six established evolutionary computation algorithms and the recently developed bio-inspired metaheuristic algorithm called Zebra Optimisation Algorithm. These algorithms include the Particle Swarm Optimisation Algorithm, the Bio-inspired Algorithms, Bat Optimisation Algorithm, Firefly Optimisation Algorithm, and Monarch Butterfly Optimisation Algorithm, as well as the Genetic Algorithm as an Evolutionary Algorithm. The dimensionality curse has been mitigated by using these metaheuristic methods for feature selection, and the results are compared with the wrapper-based feature selection XGBoost algorithm. The proposed algorithm uses the CSE-CIC -IDS2018 dataset, which contains the latest network attacks. XGBoost outperformed the other FS algorithms and was used as the feature selection algorithm. In evaluating the effectiveness of the newly proposed HXGBLSTM, binary and multi-class classifications are considered. When comparing the performance of the proposed HXGBLSTM for cyber threat detection, it outperforms seven innovative deep learning algorithms for binary classification and four of them for multi-class classification. Other evaluation criteria such as recall, F1 score, and precision have been also used for comparison. The results showed that the best accuracy for binary classification is 99.8\%, with F1-score of 99.83\%, precision of 99.85\%, and recall of 99.82\%, in extensive and detailed experiments conducted on a real dataset. The best accuracy, F1-score, precision, and recall for multi-class classification were all around 100\%, which does give the proposed algorithm an advantage over the compared ones.

An effective networks intrusion detection approach based on hybrid Harris Hawks and multi-layer perceptron

This paper proposes an Intrusion Detection System (IDS) employing the Harris Hawks Optimization algorithm (HHO) to optimize Multilayer Perceptron learning by optimizing bias and weight parameters. HHO-MLP aims to select optimal parameters in its learning process to minimize intrusion detection errors in networks. HHO-MLP has been implemented using EvoloPy NN framework, an open-source Python tool specialized for training MLPs using evolutionary algorithms. For purposes of comparing the HHO model against other evolutionary methodologies currently available, specificity and sensitivity measures, accuracy measures, and mse and rmse measures have been calculated using KDD datasets. Experiments have demonstrated the HHO MLP method is effective at identifying malicious patterns. HHO-MLP has been tested against evolutionary algorithms like Butterfly Optimization Algorithm (BOA), Grasshopper Optimization Algorithms (GOA), and Black Widow Optimizations (BOW), with validation by Random Forest (RF), XGBoost. HHO-MLP showed superior performance by attaining top scores with accuracy rate of 93.17%, sensitivity level of 89.25%, and specificity percentage of 95.41%.