Improved Moth Flame Optimization Research Articles

Long, short, and mediumterms wind speed prediction model based on LSTM optimized by improved moth flame optimization algorithm.

Time series prediction of wind speed has been widely used in wind power generation. The volatility and instability of wind speed have a large negative impact on wind turbines and power systems, which can lead to grid collapse in severe cases. Therefore, accurate wind speed prediction is crucial for wind power generation. In this paper, considering the influence of different parameters on algorithm training and prediction, an improved moth flame optimization algorithm is constructed to optimize the LSTM wind energy prediction system to obtain better performance. The system consists of three modules: data preprocessing, optimization, and prediction. The data preprocessing module uses fuzzy information granulation to blur the input data. On this basis, the combination of swarm intelligent optimization algorithm and prediction model can effectively predict wind speed time series. Taking the California wind farm as an example, the MAPE of the experiment in the short-term forecast is 3.15%, the MAPE of the medium-term forecast is 4.38%, and the MAPE of the long-term forecast is 18.28%. The experimental results show that the proposed model has obvious advantages over the previous model.

Cross-spectral vision transformer for lung nodule detection with improved moth flame algorithm using deep learning

The second-leading cause of cancer-related deaths globally is lung cancer. A patient's life may be saved if this cancer is discovered early. Cancer can be difficult to detect on computed tomography (CT) images, according to physicians. Strong nodule segmentation is a difficulty because to the variety of pulmonary nodules and the visual similarities between nodules and their surroundings. Preprocessing, feature extraction, and classification are the three phases of the approach. Gather databases first from open source platforms. Unwanted noise in acquired CT images reduces the effectiveness of classification. In order to remove undesired noise from the input image, preprocessing techniques like filtering and contrast enhancement might be taken into consideration. The essential segmentation of the image is then carried out using optimization approaches like Automatic Lung Parenchyma Mining and Remora Concavity (ALPM & RC). Cross Spectral Visual Transformer and Improved Moth Flame Optimization (CSViT-IMFO) are the two classifiers that are being suggested. For multi-process optimization in CSViT, like structural and hyperparameter optimization, IMFO is used. Utilizing performance matrices like accuracy, precision, recall, specificity, sensitivity, and F_Measure, the proposed method is put into practice in MATLAB. On the LIDC dataset, our suggested solution outperforms cutting-edge learning-based methods with a classification accuracy of 99.5 %.

An Improved Moth-Flame Algorithm for Human–Robot Collaborative Parallel Disassembly Line Balancing Problem

In the context of sustainable development strategies, the recycling of discarded products has become increasingly important with the development of electronic technology. Choosing the human–robot collaborative disassembly mode is the key to optimizing the disassembly process and ensuring maximum efficiency and benefits. To solve the problem of human–robot cooperative parallel dismantling line balance, a mixed integer programming model is established and verified by CPLEX. An improved Moth-Flame Optimization (IMFO) algorithm is proposed to speed up convergence and optimize the disassembly process of various products. The effectiveness of IMFO is evaluated through multiple cases and compared with other heuristics. The results of these comparisons can provide insight into whether IMFO is the most appropriate algorithm for the problem presented.

A dynamic weight–assignment load balancing approach for workflow scheduling in edge-cloud computing using ameliorated moth flame and rock hyrax optimization algorithms

As the geographically distributed cloud infrastructure continues to grow in scale and the intricacy of workflow applications increases, there is a growing threat to the operational efficiency of the system. This poses the risk of resource inefficiencies and elevated energy consumption. Load balancing becomes a critical concern for this category of applications, particularly when they encounter overwhelming demand surges and resource outages. Numerous comprehensive methods still overlook certain aspects, such as the inclusion of multiple objectives, addressing local minimum problems, and optimizing resource utilization to its fullest potential. Our research focus is on exploring cloud-deployed three-tier applications to address these challenges. This paper presents a new approach to load balancing, introducing a novel weight assignment algorithm. The algorithm effectively distributes tasks among virtual machines using three carefully optimized algorithms. To begin with, we convert the user tasks into the directed acyclic graph (DAG) structured workflow, organizing them according to their execution sequence. The iterative critical path search algorithm, implemented with a layer-based priority assigning technique, addresses the critical path challenge. This process involves finding a critical path, dividing tasks into distinct layers, classifying them based on their computational complexity and priorities, and distinguishing between critical and non-critical tasks. Following the classification process, the tasks are organized and prioritized using an improved Moth Flame Optimization approach based on multiple criteria, which consider criticality, priority, and makespan. This approach significantly reduces scheduling latency and delays. Finally, task allocation is proposed using the high convergence rock hyrax optimization algorithm with a divide-and-conquer strategy, enabling effective exploration and exploitation of overcrowded resources for improved load balancing. The CloudSim simulation tool is used to carry out the simulation, where it assesses various performance aspects such as latency, bandwidth, completion time, throughput, energy usage, instances of not meeting QoS violations, and overhead.

Improved moth-flame algorithm based on cat chaotic and dynamic cosine factor.

The moth-flame algorithm shows some shortcomings in solving the complex problem of optimization, such as insufficient population diversity and unbalanced search ability. In this paper, an IMFO (Improved Moth-Flame Optimization) algorithm is proposed to be applied in solving the optimization problem of function. First, cat chaotic mapping is used to generate the initial position of moth to improve the population diversity. Second, cosine inertia weight is introduced to balance the global and local search abilities of the algorithm. Third, the memory information in the particle swarm algorithm is introduced into the iterative process of the algorithm to speed up the convergence of the population. Finally, Gaussian mutation strategy is used in the current optimal solution to avoid the algorithm from falling into the local optimum. Simulation experiments are conducted on 11 benchmark test functions, compared with other improved MFO (Moth-Flame Optimization) algorithms and classical optimization algorithms. The results show that the IMFO has higher accuracy and stability in solving the above-mentioned test functions. The proposed algorithm is experimented and verified by optimizing the KELM (Kernel Extreme Learning Machine) in an engineering example and exhibits a better optimization performance.

An echo state network model with the protein structure for time series prediction

From the perspective of bionics of biological structure, this paper proposes a new reservoir topology structure with an α-helix form of the secondary protein, named S-ESN. This network model has some advantages compared with the standard leaky-echo state network (Leaky-ESN) model. Because the neurons in the traditional reservoir are randomly and sparsely connected, the stability of the echo state network (ESN) will be reduced, and the prediction accuracy will also be decreased. The S-ESN model proposed greatly improves the internal stability of the reservoir, the dynamic activity of neurons and the prediction accuracy of the ESN. At the same time, the improved moth-flame optimization algorithm (MFO) with the probability of jump disturbance is used to optimize the three parameters: the leakage rate (a), the spectral radius (ρ), and the input scaling factor (sin), which can further improve the stability and predictability of the S-ESN. In order to verify the performance of S-ESN, three virtual time series Sin time series with low frequency, Sin time series with high frequency, Mackey-Glass time series (MG) and one practical Sunspot are selected as experimental data. The experimental results show that the S-ESN model has better prediction accuracy.

Ordered Charging Planning for Electric Vehicle Clusters in Tourist Attractions Based on Improved Moth-Flame Optimization Integrated With Genetic Mechanism

Ordered Charging Planning for Electric Vehicle Clusters in Tourist Attractions Based on Improved Moth-Flame Optimization Integrated With Genetic Mechanism

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

Skin cancer detection from dermoscopic images using Deep Siamese domain adaptation convolutional Neural Network optimized with Honey Badger Algorithm

Melanoma skin cancer poses significant challenges in terms of early detection and accurate diagnosis. It often goes unnoticed in its early stages, leading to advanced diagnoses with poorer treatment outcomes. Diagnosis is subjective and can vary among healthcare professionals, while limited accessibility to dermatologists further delays diagnosis. Addressing these challenges requires innovative solutions, leveraging technology and artificial intelligence, to improve early detection, enhance accuracy, and ultimately improve patient outcomes in melanoma skin cancer. In this paper, Skin cancer identification from dermoscopic images utilizing Deep Siamese domain adaptation convolutional Neural Network optimized with Honey Badger Algorithm is proposed. The proposed method initially performs input image pre-processing to remove label noise and lighting problems. The segmentation was then given the output of the pre-processing. To separate the ROI region, hesitant fuzzy linguistic bi-objective clustering is employed. The improved non-subsampled Shearlet transforms (INSST) region to extract features using the segmented ROI region. Skin cancer and healthy skin are distinguished using the Deep Siamese domain adaptation convolutional Neural Network (DSDACNN). The Honey Badger Algorithm is used to optimize the weight parameters of the DSDACNN. The proposed SKD-DSDACNN-HBA method is carry out in Python. The performance of the proposed SKD-DSDACNN-HBA method attains 15.64%, 20.07% and 25.5% higher F1-score, 24.35%, 29.33% and 35.29% higher Computational time, 24.72%, 29.32% and 36.66% higher AUC and 33.55%, 28.52% and 19.85% lower Error rate while compared with existing methods, like fuzzy k-means clustering (SKD-FKMC), handcrafted and non-handcrafted features (SKD-HNHF) and deep learning features with improved moth flame optimization (SKD-DLF-IMFO).

Sustainable residential building energy consumption forecasting for smart cities using optimal weighted voting ensemble learning

In recent times, smart-built environments have gone through an incessant transformation, becoming more independent and sensitive ecosystems which can balance energy consumption and user comfort, whilst also achieving higher order of safety for users. The consumption of a high volume of energy in buildings has resulted in numerous environmental issues which have adverse effects on human survival. The estimation of building energy use becomes necessary to conserve energy and enhance decision-making in reducing energy usage. In addition, constructing energy-efficient buildings will help to reduce the total energy utilized in newly constructed buildings. The machine Learning (ML) technique was regarded as the most suitable method to produce favourable results in forecasting tasks. Therefore, in numerous studies, ML was implemented in the domain of energy utilization in operational buildings. This article introduces an Improved Moth Flame Optimization with Weighted Voting Ensemble Learning (IMFO-WVEL) model for Energy Consumption Forecasting in Residential Buildings. The presented IMFO-WVEL model majorly aims to forecast energy utilization in residential buildings. To accomplish this, the presented IMFO-WVEL model follows the initial stage of data preprocessing to make it compatible with further processing. To forecast the energy consumption in residential buildings, the WVEL technique comprises three DL models namely stacked autoencoder (SAE), deep neural network (DNN), and bidirectional long short-term memory (BiLSTM) is used. Finally, the IMFO algorithm is derived by the integration of MFO with the Levy flight (LF) strategy and is applied for the hyperparameter tuning process. The experimental validation of the IMFO-WVEL technique is performed under distinct aspects. The comparison study exhibited the promising performance of the IMFO-WVEL technique over recent approaches in terms of several performance measures.

Modified ADRC Design of Permanent Magnet Synchronous Motor Based on Improved Memetic Algorithm

In this paper, a novel modified auto disturbance rejection control (ADRC) design of a permanent magnet synchronous motor based on the improved memetic algorithm (IMA) is proposed. Firstly, there is an obvious system ripple caused by the defect that the optimal control function used in traditional ADRC cannot be differentiable and smooth at the segment point; aiming at weakening the system ripple effectively, the proposed method constructs a novel differentiable and smooth optimal control function to modify the ADRC design. Furthermore, aiming at improving the integration parameters optimization effect effectively, a novel improved memetic algorithm is proposed for obtaining the optimal parameters of ADRC. Specifically, an IMA with high-quality balance based on an adaptive nonlinear decreasing strategy for the convergence factor, Gaussian mutation mechanism, improved learning mechanism with the high-quality balance between competitive and opposition-based learning (OBL) and an elite set maintenance mechanism based on fusion distance is proposed so that these strategies can improve the optimization precision by a large margin. Finally, the experiment results of the PMSM speed control practical cases show that the ADRC based on IMA has an apparent better optimization effect than that of fuzzy PI, traditional ADRC based on the genetic algorithm and an improved ADRC based on improved moth-flame optimization.

Breast Cancer Classification by Gene Expression Analysis using Hybrid Feature Selection and Hyper-heuristic Adaptive Universum Support Vector Machine

Comprehensive assessments of the molecular characteristics of breast cancer from gene expression patterns can aid in the early identification and treatment of tumor patients. The enormous scale of gene expression data obtained through microarray sequencing increases the difficulty of training the classifier due to large-scale features. Selecting pivotal gene features can minimize high dimensionality and the classifier complexity with improved breast cancer detection accuracy. However, traditional filter and wrapper-based selection methods have scalability and adaptability issues in handling complex gene features. This paper presents a hybrid feature selection method of Mutual Information Maximization - Improved Moth Flame Optimization (MIM-IMFO) for gene selection along with an advanced Hyper-heuristic Adaptive Universum Support classification model Vector Machine (HH-AUSVM) to improve cancer detection rates. The hybrid gene selection method is developed by performing filter-based selection using MIM in the first stage followed by the wrapper method in the second stage, to obtain the pivotal features and remove the inappropriate ones. This method improves standard MFO by a hybrid exploration/exploitation phase to accomplish a better trade-off between exploration and exploitation phases. The classifier HH-AUSVM is formulated by integrating the Adaptive Universum learning approach to the hyper- heuristics-based parameter optimized SVM to tackle the class samples imbalance problem. Evaluated on breast cancer gene expression datasets from Mendeley Data Repository, this proposed MIM-IMFO gene selection-based HH-AUSVM classification approach provided better breast cancer detection with high accuracies of 95.67%, 96.52%, 97.97% and 95.5% and less processing time of 4.28, 3.17, 9.45 and 6.31 seconds, respectively.

Lung Nodule Detection via Optimized Convolutional Neural Network: Impact of Improved Moth Flame Algorithm.

Lung cancer is a high-risk disease that affects people all over the world, and lung nodules are the most common signof early lung cancer. Since early identification of lung cancer can considerably improve a lung scanner patient's chances of survival, an accurate and efficient nodule detection system can be essential. Automatic lung nodule recognition decreases radiologists' effort, as well as the risk of misdiagnosis and missed diagnoses. Hence, this article developed a new lung nodule detection model with four stages like "Image pre-processing, segmentation, feature extraction and classification". In this processes, pre-processing is the first step, in which the input image is subjected to a series of operations. Then, the "Otsu Thresholding model" is used to segment the pre-processed pictures. Then in the third stage, the LBP features are retrieved that is then classified via optimized Convolutional Neural Network (CNN). In this, the activation function and convolutional layer count of CNN is optimally tuned via a proposed algorithm known as Improved Moth Flame Optimization (IMFO). At the end, the betterment of the scheme is validated by carrying out analysis in terms of certain measures. Especially, the accuracy of the proposed work is 6.85%, 2.91%, 1.75%, 0.73%, 1.83%, as well as 4.05% superior to the extant SVM, KNN, CNN, MFO, WTEEB as well as GWO + FRVM methods respectively.

Preemptive multi-skill resource-constrained project scheduling of marine power equipment maintenance tasks1

The shipbuilding industry, characterized by its high complexity and remarkable comprehensiveness, deals with large-scale equipment construction, conversion, and maintenance. It contributes significantly to the development and national security of countries. The maintenance of large vessels is a complex management engineering project that presents a challenge in lowering maintenance time and enhancing maintenance efficiency during task scheduling. This paper investigates a preemptive multi-skill resource-constrained project scheduling problem and a task-oriented scheduling model for marine power equipment maintenance to address this challenge. Each task has a minimum capability level restriction during the scheduling process and can be preempted at discrete time instants. Each resource is multi-skilled, and only those who meet the required skill level can be assigned tasks. Based on the structural properties of the studied problem, we propose an improved Moth-flame optimization algorithm that integrates the opposition-based learning strategy and the mixed mutation operators. The Taguchi design of experiments (DOE) approach is used to calibrate the algorithm parameters. A series of computational experiments are carried out to validate the performance of the proposed algorithm. The experimental results demonstrate the effectiveness and validity of the proposed algorithm.

IMF 2 O 2 : A Fully Connected Sensor Deployment Algorithm for Underwater Sensor Networks

To address the problems of node deployment schemes in existing underwater sensor networks that lack consideration of network connectivity and high deployment costs, this article constructs an optimization model that maximizes network coverage and minimizes deployment costs while ensuring full connectivity. For the NP-hard property of this optimization model, an improved moth flame optimization node deployment algorithm based on fuzzy operators (IMF 2 O 2 ) is proposed. First, comprehensively considering the two performance metrics of network coverage and network connectivity, a multi-objective selection mechanism based on fuzzy operators is proposed to improve network coverage while ensuring full connectivity. Second, a fixed number of nodes are used to monitor the target event points, transforming the node deployment of sensors into an optimal problem and proposing an improved moth flame optimization algorithm to solve this problem. Finally, the two metrics of coverage and deployment cost are measured and the fuzzy operator is used to select the optimal number of nodes to be deployed. Numerical results showed that the proposed algorithm improved network coverage rate by 10%, 22%, and 25%, and improved network connectivity rate by 12%, 20%, and 8% as compared to PSSD, RAWS, and VODA, respectively, while ensuring full connectivity.

Identification of influential users with cost minimization via an improved moth flame optimization

The issue of influence maximization has received great attention due to its application potential. Some traditional models used to solve the influence maximization problem only consider the maximum propagation range that the seed node set can reach but ignore the cost difference between the potential candidate nodes. This is not characteristic of real-world network behavior. Thus, in response to this research gap, a multi-objective optimization model based on maximizing influence spreading while minimizing cost is proposed in this paper. On the basis of maintaining the effective characteristics of non-dominated sorting moth flame optimization, the diversity weight and mutation mechanism are integrated into the algorithm to maintain the population diversity in the exploration stage. The evolutionary rules of the original moth flame optimization are redesigned to meet the desired needs of multi-objective influence optimization. By considering three types of real-world social networks, we show that our proposed method can generate a set of well-distributed Pareto optimal solutions.

Parameters estimation of AC transmission line by an improved moth flame optimization method

The parameters calculation for a transmission line is described in the proposed work, and the estimation of transmission line parameters is essential for properly managing transmission and distribution networks. The research work deals with the concept of optimization of the transmission line parameters. To this purpose, an improved moth flame optimization technique is applied to different test cases of the transmission lines. Moth movement with respect to the light source is the basis for improved moth flame optimization. In this research work, simulations are performed on various test cases to determine the effectiveness of the suggested method. Results from the suggested method are compared with those from other methods mentioned in the literature. The comparison shows that the proposed method rapidly and smoothly converges to the best-obtained value. The improved moth flame optimization performs better than other algorithms in terms of solution quality and feasibility, proving its effectiveness and competence. The optimal values obtained for capacitance in (mathrm{mu F}) are 0.22406, 0.022935, and 0.0049734, and for inductance in (mH) are 0.65055, 0.41258, and −0.76593, respectively.

Mathematical formulation and an improved moth–flame optimization algorithm for parallel two-sided disassembly line balancing based on fixed common stations

Abstract Nowadays, rapid product iterations result in large quantities of end-of-life products. To meet the fast-growing demand for remanufacturing engineering, companies have quickened the standardization and industrialization of waste dissembling. Two-sided disassembly lines can effectively disassemble large-sized products on both sides of the lines, and parallel disassembly lines can disassemble multiple products simultaneously with fewer workstations and higher production efficiency. Combining the two types of disassembly can effectively increase the disassembly efficiency of large-sized products. However, the parallel two-sided disassembly line has not been fully investigated because of the essential complexity of the problem. Therefore, this research introduced the parallel two-sided disassembly line balancing problem with fixed common stations. First, a multi-objective mixed-integer programming model is established to solve the problem for the first time. The model is proved to be correct through small-scale numerical examples. Second, a multi-objective improved moth–flame optimization algorithm is implemented to solve the proposed large-scale problems. The proposed algorithm employs a two-phase decoding approach to design the scheme and a discrete moth for fire operation to search and replace new individuals, and then a restart strategy is introduced to reduce the probability of the population falling into a local optimum. Finally, the algorithm solved extensive disassembly line balancing problems with different layouts, including the straight-line, two-sided, and parallel two-sided, and case studies demonstrated the reliability and validity of the proposed method.

Mutational Chemotaxis Motion Driven Moth-Flame Optimizer for Engineering Applications

Moth-flame optimization is a typical meta-heuristic algorithm, but it has the shortcomings of low-optimization accuracy and a high risk of falling into local optima. Therefore, this paper proposes an enhanced moth-flame optimization algorithm named HMCMMFO, which combines the mechanisms of hybrid mutation and chemotaxis motion, where the hybrid-mutation mechanism can enhance population diversity and reduce the risk of stagnation. In contrast, chemotaxis-motion strategy can better utilize the local-search space to explore more potential solutions further; thus, it improves the optimization accuracy of the algorithm. In this paper, the effectiveness of the above strategies is verified from various perspectives based on IEEE CEC2017 functions, such as analyzing the balance and diversity of the improved algorithm, and testing the optimization differences between advanced algorithms. The experimental results show that the improved moth-flame optimization algorithm can jump out of the local-optimal space and improve optimization accuracy. Moreover, the algorithm achieves good results in solving five engineering-design problems and proves its ability to deal with constrained problems effectively.

Research on Green Reentrant Hybrid Flow Shop Scheduling Problem Based on Improved Moth-Flame Optimization Algorithm

To address the green reentrant hybrid flow shop-scheduling problem (GRHFSP), we performed lifecycle assessments for evaluating the comprehensive impact of resources and the environment. An optimization model was established to minimize the maximum completion time and reduce the comprehensive impact of resources and the environment, and an improved moth-flame optimization algorithm was developed. A coding scheme based on the number of reentry layers, stations, and machines was designed, and a hybrid population initialization strategy was developed, according to a situation wherein the same types of nonequivalent parallel machines were used. Two different update strategies were designed for updating the coding methods of processes and machines. The population evolution strategy was adopted to improve the local search ability of the proposed algorithm and the quality of the solution. Through simulation experiments based on different datasets, the effectiveness of the proposed algorithm was verified, and comparative evaluations revealed that the proposed algorithm could solve the GRHFSP more effectively than other well-known algorithms.