Moth Flame Optimization Research Articles

An Efficient Spam Mail Classification Approach Using Improved Moth Flame Optimization and Multi-class Support Vector Machine

An Efficient Spam Mail Classification Approach Using Improved Moth Flame Optimization and Multi-class Support Vector Machine

Optimal Power Flow of Power System with Static VAR Compensator Using Moth Flame Optimization

Optimal Power Flow of Power System with Static VAR Compensator Using Moth Flame Optimization

A resilient and intelligent multi-objective energy management for a hydrogen-battery hybrid energy storage system based on MFO technique

In this paper, a new design and flexible energy management strategy are presented for microgrids. The proposed intelligent energy management system (IEMS) achieves effective integration between the resilient microcontroller, chosen for its rapid response speed and its capability to perform multiple operations simultaneously, and the optimization techniques to enhance the power quality. The IEMS is designed using the FPGA board, chosen for its flexibility and capability to handle multiple and complex operations simultaneously. The experimental testing of the IEMS demonstrates a significant level of effectiveness in managing energy. To enhance system performance and ensure cost-effective reliability, advanced optimization techniques are employed. This study deals with a complex multi-objective optimization problem involving the limitations of energy generation, load demand, and a hydrogen-battery hybrid energy storage system. The moth-flame optimization (MFO) algorithm is chosen to solve this optimization problem due to its rapid convergence rate and accuracy. The effectiveness of the MFO algorithm is assessed by comparing it with several new algorithms. The obtained results show the robust performance of the IEMS and its high responsiveness to dynamic operational scenarios. It can observe, gather, and analyze data in real-time. It achieves a remarkable 1.287 % reduction in operating costs within a short timeframe.

Integrated approach for dynamic economic/emission dispatch problem: multi-objective moth flame optimizer with transmission loss prediction using cascaded forward neural network

Integrated approach for dynamic economic/emission dispatch problem: multi-objective moth flame optimizer with transmission loss prediction using cascaded forward neural network

Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes.

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves as a prevalent diagnostic tool in prenatal care for monitoring fetal growth and development. Nevertheless, acquiring standard fetal ultrasound planes with accurate anatomical structures proves challenging and time-intensive, even for skilled sonographers. Therefore, for determining common maternal fetuses from ultrasound images, an automated computer-aided diagnostic (CAD) system is required. A new residual bottleneck mechanism-based deep learning architecture has been proposed that includes 82 layers deep. The proposed architecture has added three residual blocks, each including two highway paths and one skip connection. In addition, a convolutional layer has been added of size 3 × 3 before each residual block. In the training process, several hyper parameters have been initialized using Bayesian optimization (BO) rather than manual initialization. Deep features are extracted from the average pooling layer and performed the classification. In the classification process, an increase occurred in the computational time; therefore, we proposed an improved search-based moth flame optimization algorithm for optimal feature selection. The data is then classified using neural network classifiers based on the selected features. The experimental phase involved the analysis of ultrasound images, specifically focusing on fetal brain and common maternal fetal images. The proposed method achieved 78.5% and 79.4% accuracy for brain fetal planes and common maternal fetal planes. Comparison with several pre-trained neural nets and state-of-the-art (SOTA) optimization algorithms shows improved accuracy.

An adaptive ranking moth flame optimizer for feature selection

Feature selection is to identify informative and concise sub-features from raw datasets, which can be modelled as an optimization issue. An adaptive ranking moth-flame optimization (ARMFO) is developed to solve the problem. The proposed ARMFO algorithm has five improvements: the ranking probability divides moths into better and worse groups; each group performs appropriate position-update equations to enhance the local and global search; a self-adaptive chaotic mutation is used to increase the quality of the best flame; a greedy selection is to maintain better solutions, and the structure of flames is changed. The search ability of the ARMFO algorithm is verified on a test suit, and the algorithm has obtained the best results on twenty-one functions, which accounts for 72.41%. Then, the proposed ARMFO algorithm and seven swarm intelligent algorithms are used for feature selection on fourteen datasets from UCI. The proposed ARMFO algorithm has obtained satisfactory results on 9 datasets compared to its seven rivals.

Hybrid machine learning models for classifying failure modes of unstiffened steel plate girders subjected to patch loading

This paper, for the first time, develops novel hybrid machine learning models that combine Support Vector Machine, Naïve Bayes, K-Nearest Neighbors, Decision Tree, Random Forest, Adaptive Boosting, Gradient Boosting, and Extreme Gradient Boosting, with Moth-Flame Optimization algorithm, called MFO-SVM, MFO-NB, MFO-KNN, MFO-DT, MFO-RF, MFO-AB, MFO-GB, and MFO-XGB, respectively, to classify the failure modes of unstiffened steel plate girders subjected to patch loading. The experimental database is collected from the tests reported in the literature to develop the hybrid machine learning models. The Synthetic Minority Over-sampling Technique is adopted to address the imbalanced class problems of the database. Hyperparameters of machine learning models are optimized using the Moth-Flame Optimization algorithm. The performance of hybrid machine learning models is evaluated and compared through the confusion matrix using several metrics. The results show that the MFO-RF model proves more effective than other models in identifying the failure models of unstiffened steel plate girders. The Shapely Additive Explanations method is used to analyze the contribution of each variable to the failure modes of unstiffened steel plate girders both globally and locally. Web thickness is the most influencing feature for identifying the failure modes. The MFO-RF model is finally utilized to develop a web application for practical use at low cost and effort.

Automatic epileptic seizure detection based on EEG using a moth-flame optimization of one-dimensional convolutional neural networks.

Frequent epileptic seizures can cause irreversible damage to the brains of patients. A potential therapeutic approach is to detect epileptic seizures early and provide artificial intervention to the patient. Currently, extracting electroencephalogram (EEG) features to detect epileptic seizures often requires tedious methods or the repeated adjustment of neural network hyperparameters, which can be time- consuming and demanding for researchers. This study proposes an automatic detection model for an EEG based on moth-flame optimization (MFO) optimized one-dimensional convolutional neural networks (1D-CNN). First, according to the characteristics and need for early epileptic seizure detection, a data augmentation method for dividing an EEG into small samples is proposed. Second, the hyperparameters are tuned based on MFO and trained for an EEG. Finally, the softmax classifier is used to output EEG classification from a small-sample and single channel. The proposed model is evaluated with the Bonn EEG dataset, which verifies the feasibility of EEG classification problems that involve up to five classes, including healthy, preictal, and ictal EEG from various brain regions and individuals. Compared with existing advanced optimization algorithms, such as particle swarm optimization, genetic algorithm, and grey wolf optimizer, the superiority of the proposed model is further verified. The proposed model can be implemented into an automatic epileptic seizure detection system to detect seizures in clinical applications.

An implementation of inertia control strategy for grid-connected solar system using moth-flame optimization algorithm

As the solar power system power system grows rapidly, inertia control strategy (ICS) becomes crucial to enable stable grid integration. However, the existing ICS lacks of dynamic weather analysis with maximum power point tracking (MPPT) and fault-ride through (FRT) capabilities such as low voltage ride-through (LVRT) and high voltage ride-through (HVRT). In this work, an inertia weighting strategy and the Cauchy mutation operator are introduced to improve the moth-flame optimization (MFO) algorithm to support vector machine prediction of photovoltaic power generation. In this paper, the proposed adaptive VICS with variable moment of inertia (J) and damping factor ( D P ) demonstrates its effectiveness with faster frequency recovery, less overshooting and continuous stable operation under grid fault and dynamic weather. The MFO algorithm is used to implement inertia control strategies for grid-connected solar systems. Accurate simulation results confirm the inertia control of the emulsion and the control of the solar system. The results of the simulation show a significant improvement in frequency with the designed MFO and compared to Horse Herd Optimization (HHO). The proposed method contributes to improve photovoltaic energy prediction, reduces the impact of photovoltaic power penetration into the grid and maintains the system reliability.

Multi-objective quasi-reflection learning and weight strategy-based moth flame optimization algorithm

Multi-objective quasi-reflection learning and weight strategy-based moth flame optimization algorithm

Epidermis lesion detection via optimized distributed capsule neural network

Skin cancer, encompassing various forms such as melanoma, basal cell carcinoma, and others, remains a significant global health concern, often proving fatal if not diagnosed and treated in its early stages. The challenge of accurately diagnosing skin cancer, particularly melanoma, persists even for experienced dermatologists due to the intricate and unpredictable nature of its symptoms. To address the need for more accurate and efficient skin cancer detection, a novel Golden Hawk Optimization-based Distributed Capsule Neural Network (GHO-DCaNN) is proposed. This novel technique leverages advanced computational methods to improve the reliability and precision of skin cancer diagnosis. An optimized clustering-based segmentation approach is introduced, integrating the innovative Sewer Shad Fly Optimization (SSFO), which combines elements of both mayfly and moth flame optimization. This integration enhances the accuracy of lesion boundary delineation and feature extraction. The core of the innovation lies in the optimized distributed capsule neural network, which is trained using the Hybrid GHO. This optimizer, inspired by the behaviors of the golden eagle and fire hawk, ensures the effectiveness of epidermis lesion detection, pushing the boundaries of skin cancer diagnosis methods. The achievements based on the metrics, like specificity, sensitivity, and accuracy show 97.53%, 99.05%, and 98.83% for 90% of training and 97.83%, 99.50%, and 99.06% for k-fold of 10, respectively.

Grey wolf optimizer for the design optimization of a DC-DC boost converter

Design optimization of a DC-DC boost converters for minimizing overall operational losses is the problem under study. The optimum design contains selecting the best value of circuit inductance, capacitance, and switching frequency for the continuous conduction mode (CCM) operation. The design constraints selected are the current ripple content, ripple content of voltage, and bandwidth for operation in CCM. Grey wolf optimizer (GWO) algorithm is implemented for the boost DC-DC converter’s optimal design. The comparative investigation of algorithms reveals that GWO outperforms other established optimization algorithms like the particle swarm optimization (PSO), moth flame optimization (MFO) algorithm, simulated annealing (SA) and firefly algorithm (FFA) in terms of convergence time, computational effort, and the minimized system losses while finding the most efficient design of the converter. Parametric study on statistical performance indicates that the GWO algorithm outperforms the rest of the optimization algorithms on comparing with previously reported results. The optimized results obtained in the current work provides and improved converter design with reduction in the power loss in the optimized design of the converter by over 15% from previously reported works.

Lithium-Ion Battery Optimal Charging Using Moth-Flame Optimization Algorithm and Fractional-Order Model

Considering the increasingly serious environmental pollution and energy crisis, new energy electric vehicles have good application prospects in future transportation. The lithium-ion battery is a kind of energy storage device widely used in electric vehicles. An efficient and optimal charging strategy is the premise of its extensive use. In this paper, a fractional model-based multi-stage charging strategy using Moth-flame Optimization (MFO) Algorithm is presented, in which the charging current is divided into several stages. Considering the convenience and safety of the charging process, the charging time, temperature rise, and energy loss are selected as optimization objectives. The MFO algorithm, which has the advantages of simple structure and excellent local search capacity, is employed to optimize the charging performance. Through experiments and simulations, the impacts of the current stage number, cut-off voltage, and weights of each fitness function part on the charging effect are discussed respectively. This paper provides guidance for optimal charging of lithium-ion batteries under different demands in practice.

An atom search optimization approach for IIR system identification

ABSTRACT Filtering, or digital signal processing, is a significant and fundamental requirement in fields such as signal systems and computers. The process of designing optimal digital filters is difficult, which has led researchers to design filters using emerging evolutionary computations. Metaheuristics have emerged as the most promising tool for solving optimization problems, with excellent development and improvement. However, it has not been clear how to select the best performing metaheuristic to design an optimal digital filter. In this paper, a digital infinite impulse response (IIR) filter is constructed using the atom search optimization (ASO) algorithm impressed by the physical motion of atoms in nature based on molecular dynamics. The simulation results obtained are extensively compared with the results of other optimization algorithms such as moth flame optimization, gravitational search algorithm and artificial bee colony optimization. ASO was found to have the highest percentage of improvement. Furthermore, eight cases are analyzed across four numerical filter instances with the same degree and four with reduced degree, and the results are validated by outperforming several different algorithm-based approaches in the literature. The stability analysis on the basis of pole zero diagrams further cements the efficacy of the ASO for IIR system identification problem.

Failure Mode Identification and Shear Strength Prediction of Rectangular Hollow RC Columns Using Novel Hybrid Machine Learning Models

Failure mode identification and shear strength prediction are critical issues in designing reinforced concrete (RC) structures. Nevertheless, specific guidelines for identifying the failure modes and for accurate predictions of the shear strength of rectangular hollow RC columns are not provided in design codes. This study develops hybrid machine learning (ML) models to accurately identify the failure modes and precisely predict the shear strength of rectangular hollow RC columns. For this purpose, 121 experimental results of such columns are collected from the literature. Eight widely used ML models are employed to identify the failure modes and predict the shear strength of the column. The moth-flame optimization (MFO) algorithm and five-fold cross-validation are utilized to fine-tune the hyperparameters of the ML models. Additionally, seven empirical formulas are adopted to evaluate the performance of regression ML models in predicting the shear strength. The results reveal that the hybrid MFO-extreme gradient boosting (XGB) model outperforms others in both classifying the failure modes (accuracy of 93%) and predicting the shear strength (R2 = 0.996) of hollow RC columns. Additionally, the results indicate that the MFO-XGB model is more accurate than the empirical models for shear strength prediction. Moreover, the effect of input parameters on the failure modes and shear strength is investigated using the Shapley Additive exPlanations method. Finally, an efficient web application is developed for users who want to use the results of this study or update a new dataset.

Hybrid metaheuristic model based performance-aware optimization for map reduce scheduling

Because of the rapid rise in the count of corporations using cloud-dependent infrastructure as the foundation for big data storing and analysis. The fundamental difficulty in scheduling big data services in cloud-dependent systems is ensuring the shortest possible makespan while simultaneously reducing the number of resources being used. We have created a new, secure map reduce scheduling method that functions as follows. Initially, the cloud architecture is designed and the tasks are generated. In the pre-processing phase, the huge set of tasks was processed by the map-reduce scheduling framework. Afterward, the optimal task scheduling task is conducted which utilizes a hybrid algorithm named Tunicate Combined Moth Flame Algorithm (TCMFA), which provides better task scheduling via providing optimal makespan, execution time, and security. This proposed TCMFA is the hybridization of both Moth Flame Optimization (MFO) and Tunicate Swarm Algorithm (TSA). The error rate of the TCMFA gets reduced to 320 approximately over other conventional methods such as RSA, ACO, GHO, BTS, OWPSO, BES, PRO, SOA, COOT, TSA & MFO which proves the accuracy of our TCMFA and makes it more efficient and secure for optimal map-reduce scheduling.

An effective process of VM migration with hybrid heuristic-assisted encryption technique for secured data transmission in cloud environment

The virtualization of hardware resources like network, memory and storage are included in the core of cloud computing and are provided with the help of Virtual Machines (VM). The issues based on reliability and security reside in its acceptance in the cloud environment during the migration of VMs. VM migration highly enhanced the manageability, performance, and fault tolerance of cloud systems. Here, a set of tasks submitted by various users are arranged in the virtual cloud computing platform by using a set of VMs. Energy efficiency is effectively attained with the help of a loadbalancing strategy and it is a critical issue in the cloud environment. During the migration of VMs, providing high security is a very important task in the cloud environment. To resolve such challenges, an effective method is proposed using an optimal key-based encryption process. The main objective of this research work is to perform the VM migration and derive the multi-objective constraints with the help of hybrid heuristic improvement. The optimal VM migration is achieved by the hybrid algorithm as Improved Binary Battle Royale with Moth-flame Optimization (IBinBRMO). It can also be used to derive the multi-objective functions by some constraints like resource utilization, active servers, makespan, energy consumption, etc. After VM migration, the data transmission should take place securely between the source and destination. To secure the data, the HybridHomophorphic and Advanced Encryption Standard(HH-AES) Algorithm, where IBinBRMO optimizes the key. After optimizing the keys, the data are securely transformed along with multi-objective functions using parameters includingthe degree of modification, hiding failure rate and information preservation rate. Thus, the effectiveness is guaranteed and analyzed with other classical models. Hence, the results illustrate that the proposed work attains better performance.

Hierarchical bi-level load frequency control for multi-area interconnected power systems

To overcome the shortcomings of proportional–integral (PI) control, a linear quadratic regulator (LQR) is proposed in this study, to determine the set points of the PI controllers in a two-level hierarchical manner. The application of the proposed scheme is studied on load frequency control (LFC) of a power system with multiple control areas (CAs). At the lower control level, a discrete PI controllers are used to regulate the frequency and tie-line powers of each of the CAs. To achieve an improved closed loop performance, the local PI controllers are optimally tuned using moth flame optimization (MFO) algorithm by minimizing the Integral Square Error (ISE) of the state errors with respect to local information and interactions with neighboring CAs. While at the upper control layer, an optimized finite-horizon LQR is applied as a guide to establish the optimal set-points of the lower level PI controllers. Since only few of the state variables can be accessed, Kalman filter (KF) is applied as an observer for the estimation of the remaining states. The efficacy of the proposed scheme is verified by implementing it on a three-CA system perturbed with a step and random net disturbances formed by combining the fluctuations in the output power of DFIG-based wind turbine generator and random load demand. From the simulation results, it is established that the proposed LQR-PI supervisory scheme has outperformed the conventional PI control scheme in optimality and stability.

A BMO‐based MRPID controller with optimal control of speed in hybrid stepper motor

AbstractThis paper proposes a Barnacles mating optimizer‐based multi‐resolution proportional‐integral derivative (MRPID) controller for precise speed control of the hybrid stepper motor (HSM). The proposed approach is a barnacle mating optimizer (BMO) control scheme. The main objective of this approach is to use the MRPID controller to improve speed control in particular and uncertain conditions. The BMO is utilized to create the proposed MRPID controller. The proposed converter has a low switching voltage and uses a low input current. The proposed converter supplies a large amount of power to the voltage source inverter (VSI), which converts DC to AC and then supplies it to the HSM. The HSM can be utilized in various settings, including robots and factory applications. Then, the performance of the proposed system has been evaluated in the MATLAB platform and compared with various existing systems. The existing adaptive neuro‐fuzzy inference system (ANFIS) and the moth flame optimization algorithm (MFO) methods are used to validate the efficiency of the proposed controller. The proposed system rise time is 0.0007, the settling time is 0.1, the recovery time is 0.221, the AMU is 1.205, the IAE is 0.1034, and the SSE is 0.234. According to the simulation findings, the suggested system is statistically significant.

Total Harmonic Distortion Reduction in Multilevel Inverters through the Utilization of the Moth–Flame Optimization Algorithm

This paper shows the implementation of the Moth–Flame Optimization algorithm in a Cascade-H multilevel inverter with five and seven levels to determine the optimal switching sequence of the inverter’s semiconductor devices. The algorithm was coded in Matlab software, and the obtained switching sequences were implemented in a Cascade-H multilevel inverter laboratory prototype, where the output voltage waveform was obtained using a digital oscilloscope. The experimental Total Harmonic Distortion was obtained using a power quality analyzer. The experimental results show the improvement of the Total Harmonic Distortion in the voltage output. These results were compared with other papers in the literature with different metaheuristic methods concerning the same modulation. These findings demonstrate the feasibility of employing the Moth–Flame Optimization Algorithm to significantly reduce the Total Harmonic Distortion, obtaining a lower value than most analyzed papers.