Levy Flight Research Articles

Levy Flight Equilibrium Optimizer–Based Wing Design Optimization Using Functional Constraints

This paper presents wing design optimization with functional constraints using the proposed method. The proposed method is an equilibrium optimizer (EO), and it is motivated by controlling the volume mass balance method to calculate both equilibrium and dynamic states. A well-defined generation rate term is verified to strengthen the capability of the EO at exploitation and exploration. Here, the EO’s updating behavior is enhanced by Levy flight. Hence, it is named as Levy flight–based equilibrium optimizer (LFEO). The analysis device of aerodynamics is intended by combining the numerical nonlinear lifting-line method to attain the coefficients of lift and drag of the baseline wing. The proposed method is utilized for solving restricted optimization problems, and three various optimization problems are solved. The functional restrictions are connected to wing weight, wing plan form area, and root bending moment, which is included in the first optimization issue, and then the second optimization issue is created. The third optimization issue is attained by including to the second issue the state of level flight and minimum speed of the baseline unmanned air vehicle (UAV) at the maximum speed of level flight and obtainable power restrictions. It shows that describing the moment of root bending, wing area, and obtainable power restrictions at aerodynamic optimization issues may lead to various realistic wing-plane and airfoil shapes. Finally, the proposed method is implemented using MATLAB or Simulink and it is compared with existing techniques such as EO and genetic algorithm (GA).

Daily load prediction study on SCASL and BiGRU models

In the short-term power load prediction, the environmental uncertainty will have an impact on the prediction accuracy and increase the complexity of the prediction. To deal with this problem, the paper proposes a bidirectional gated recurrent unit (BiGRU) network model for and sine cosine optimization algorithm with self-learning strategy and Levy flight (SCASL). With real power load data as a data set, high correlation parameters are selected through the Pearson correlation coefficient analysis as input, using the SCASL algorithm and nonlinear weight factor. Finally, the key time points in capturing the historical information of the load changes are established according to the optimization parameters, and BiGRU model is used to learn to predict its accuracy. The experimental results show that the MAPE (Mean Absolute Percentage Error) and RMSE (Root Mean Square Error) of BiGRU model are better than the comparison model gated recurrent unit (GRU) network model. The final results show that the prediction accuracy of the power load system has been significantly improved.

MAXIMUM POWERPOINT TRACKING IN SOLAR PANELS DURING PARTIAL SHADING

For maximum utilization of solar energy, photovoltaic (PV) power systems should be operated at the maximum power point (MPP) which can be achieved using maximum power point tracking (MPPT) methods. However, the occurrence of multi-peak on P-V curve of a PV array due to the changing environmental conditions such as being partially shaded increases the complexity of the tracking process. The global MPP cannot always be achieved by the conventional MPPT methods. Therefore, a novel MPPT method for PV systems using flower pollination (FP) algorithm is proposed in this paper and the Levy flight is used to improve the convergence of FP algorithm. MPPT model of the PV system is established in MATLAB to verify the effectiveness of the proposed method, and the proposed method is compared with two well established MPPT methods. The simulation results indicate that the proposed MPPT method can quickly track the changes in external environment and effectively handle the partially shaded condition. Key Words: Photovoltaic (PV), maximum power point tracking (MPPT), partially shaded condition, flower pollination (FP)

Adaptive Bandwidth Allocation for Massive MIMO Systems Based on Multiple Services

Aiming at the characteristics of resource periodicity in massive MIMO systems and bandwidth allocation without comprehensive consideration of user service QoS and channel state information, resulting in poor user satisfaction and low bandwidth utilization, this paper proposes an adaptive bandwidth allocation method based on user services. This method comprehensively considers factors, such as user service QoS, channel state information, and resource periodicity, to adaptively allocate bandwidth for users using different services. Firstly, based on the service priority, the user priority is dynamically adjusted according to the current channel state information and the continuous periodicity of the allocation, and the user is scheduled.; Secondly, the dynamic priority is combined with the minimum guaranteed time slot to establish the objective function of adaptive bandwidth allocation. Finally, chaos theory, Levy flight, and reverse learning are integrated to improve the bald eagle optimization algorithm. The improved bald eagle algorithm is used to solve the problem, and the optimal solution to bandwidth allocation is obtained. The simulation shows that compared with the traditional bandwidth allocation method based on user service quality perception, the bandwidth allocation algorithm based on the minimum rate requirement, and the ant colony-based allocation algorithm, the bandwidth allocation method proposed in this paper improves the system utility value, bandwidth utilization rate, throughput, and user satisfaction by 23.70%, 4.22%, 6.55%, and 4.28%, respectively, and better meets the business needs of users.

UAV hyperspectral remote sensor images for mango plant disease and pest identification using MD-FCM and XCS-RBFNN.

To diminish disease transmission together with promoting effective management techniques, it is crucial to monitor plant health and detect pathogens earlier. The initial part in reducing losses sourced from plant diseases is to make an accurate and earlier identification. Thus, the usage of unmanned aerial vehicle (UAV) hyperspectral imaging (HSI) sensors for surveying and assessing crops, orchards, and forests has rapidly elevated over the last decade, particularly for the stress management like water, diseases, nutrition deficits, and pests. Using Minkowski Distance-based Fuzzy C Means (MD-FCM) clustering and Xavier initialization-adapted Cosine Similarity-induced Radial Bias Function Neural Network (XCS-RBFNN) techniques, a UAV HS imaging remote sensor for Spatial and Temporal Resolution (STR) of mango plant disease and pest identification is proposed in this scheme. Collecting the input UAV source (image or video) is eventuated initially along with the Region of Interest (ROI) calculated which is followed by preprocessing. Leaf segmentation is eventuated using Logistic U-net after preprocessing. Next, MD-FCM performs clustering to cluster the diseased leaves and pests individually. The disease and pest characteristics are then retrieved separately and classified further. The requisite features are then chosen from the retrieved features utilizing the Levy Flight Distribution-produced Butterfly Optimization Algorithm (LFD-BOA). Finally, the XCS-RBFNN classifier is utilized to categorize the various diseases together with pests found in the UAV input source using the chosen features. The proposed framework's experimental findings are then compared to some prevailing schemes, with the results revealing that the proposed work outperforms other benchmark techniques.

Optimized Deeplearning Algorithm for Software Defects Prediction

Accurate software defect prediction (SDP) helps to enhance the quality of the software by identifying potential flaws early in the development process. However, existing approaches face challenges in achieving reliable predictions. To address this, a novel approach is proposed that combines a two-tier-deep learning framework. The proposed work includes four major phases:(a) pre-processing, (b) Dimensionality reduction, (c) Feature Extraction and (d) Two-fold deep learning-based SDP. The collected raw data is initially pre-processed using a data cleaning approach (handling null values and missing data) and a Decimal scaling normalisation approach. The dimensions of the pre-processed data are reduced using the newly developed Incremental Covariance Principal Component Analysis (ICPCA), and this approach aids in solving the “curse of dimensionality” issue. Then, onto the dimensionally reduced data, the feature extraction is performed using statistical features (standard deviation, skewness, variance, and kurtosis), Mutual information (MI), and Conditional entropy (CE). From the extracted features, the relevant ones are selected using the new Euclidean Distance with Mean Absolute Deviation (ED-MAD). Finally, the SDP (decision making) is carried out using the optimized Two-Fold Deep Learning Framework (O-TFDLF), which encapsulates the RBFN and optimized MLP, respectively. The weight of MLP is fine-tuned using the new Levy Flight Cat Mouse Optimisation (LCMO) method to improve the model's prediction accuracy. The final detected outcome (forecasting the presence/ absence of defect) is acquired from optimized MLP. The implementation has been performed using the MATLAB software. By using certain performance metrics such as Sensitivity, Accuracy, Precision, Specificity and MSE the proposed model’s performance is compared to that of existing models. The accuracy achieved for the proposed model is 93.37%.

Supervisory Control of Solar-Wind-Biomass-Fuel Cell Energy System for Optimal Performance

Generating electricity through a hybrid renewable energy system (HRES) plays a crucial role in achieving the sustainable development goal of affordable and clean energy (SDG 7). However, designing an optimal HRES is challenging due to the fluctuating demand and intermittent nature of renewable energy sources (RES). In recent times, hybrid hydrogen-battery energy storage technologies have garnered significant attention as they offer a pathway to a sustainable HRES with zero net emissions. This research paper introduces a rule-based algorithm and a metaheuristic optimization technique called Levy Flight Algorithm (LFA) for the energy management strategy (EMS) of an independent HRES. The EMS aims to establish a sequence for power delivery among the different components within the microgrid. The LFA is employed to optimize this EMS. To account for the variability and unpredictability of renewable energy sources, the proposed EMS is evaluated across four scenarios: winter, spring, summer, and autumn. These scenarios are derived from a stochastic model of RES. The results of the evaluation demonstrate that the energy management strategy implemented to control the HRES has successfully established an environmentally friendly energy system.

An improved equilibrium optimizer for numerical optimization: A case study on engineering design of the shell and tube heat exchanger

The accurate design of the shell-and-tube heat exchanger (STHE) model is crucially important for industries such as process industries, oil refining, thermic devices, and power plants. However, determining its parameters is a challenging task due to the multimodality and nonlinearity of the cost function. Due to such nature, performing algorithms face the challenges of sluggish convergence speed and the proneness to local optima, making it difficult to reach satisfactory solutions. In line with the above challenges, this paper aims to contribute by developing an improved optimization algorithm that can effectively deal with such optimization issues. The proposed algorithm, named Levy-Opposition- Equilibrium Optimizer (LOEO), combines the Equilibrium Optimizer (EO) and the strategies of opposition learning (OL) and Levy flight learning (LFL) to overcome the dilemma of early convergence and getting trapped in local optima. It does this by employing the OL and LFL strategies after performing the EO phase to explore the search space in opposite directions and exploit the search space by means of the Levy distribution pattern, respectively. The proposed LOEO approach aims to enhance the algorithm's ability (provides enhanced exploration capability, effective search space exploitation, and enhanced convergence speed) and reach higher quality solutions to optimization problems. To assess the performance of the suggested LOEO, three stages of analysis are carried out. In the first stage, the performance of the proposed algorithm is investigated on some benchmark problems, including CEC 2005 and CEC 2020, using statistical verifications, convergence behavior, and the boxplot paradigm. The second stage involves the non-parametric Friedman test to assess the significance of results, where the results indicate that the LOEO outperforms the best existing one (equilibrium whale optimization algorithm (EWOA)) by an average rank of the Friedman test greater than 28% for CEC 2005 benchmark functions while outperforming enhanced EO (EEO) by 24% for CEC 2020. Finally, the LOEO's applicability is realized through determining the optimal design structure of the STHE model. The reported results indicate that the LOEO algorithm offers accurate performance compared to competing algorithms as it saves the cost of the device by 39.81% compared to the best existing results, and thus, it is commended to adopt for new applications.

Aftermath epidemics: Percolation on the sites visited by generalized random walks.

We study percolation on the sites of a finite lattice visited by a generalized random walk of finite length with periodic boundary conditions. More precisely, consider Levy flights and walks with finite jumps of length >1 [like Knight's move random walks (RWs) in two dimensions and generalized Knight's move RWs in 3D]. In these walks, the visited sites do not form (as in ordinary RWs) a single connected cluster, and thus percolation on them is nontrivial. The model essentially mimics the spreading of an epidemic in a population weakened by the passage of some devastating agent-like diseases in the wake of a passing army or of a hurricane. Using the density of visited sites (or the number of steps in the walk) as a control parameter, we find a true continuous percolation transition in all cases except for the 2D Knight's move RWs and Levy flights with Levy parameter σ≥2. For 3D generalized Knight's move RWs, the model is in the universality class of pacman percolation, and all critical exponents seem to be simple rationals, in particular, β=1. For 2D Levy flights with 0<σ<2, scale invariance is broken even at the critical point, which leads at least to very large corrections in finite-size scaling, and even very large simulations were unable to unambiguously determine the critical exponents.

Improved Reptile Search Algorithm for Optimal Design of Solar Photovoltaic Module

This study focuses on the vital role of parameter extraction in optimizing and evaluating solar photovoltaic (PV) systems, as it directly influences their efficiency in converting solar energy to electricity. Researchers have extensively explored the application of various metaheuristic algorithms to accurately estimate solar PV parameters due to their crucial significance, leading to an extensive body of literature on the subject. However, the search for a robust and user-friendly optimizer with high convergence ability remains a challenging task that demands further research. To address this challenge, the study conducts a comprehensive comparative analysis of the RSALF optimizer, an innovative metaheuristic algorithm combining the reptile search algorithm (RSA) with Lévy flight (LF), for parameter extraction of PV model parameters using the Photowatt-PWP201 PV module as a case study. The experimental results demonstrate the RSALF optimizer's remarkable accuracy in parameter estimation, consistently yielding lower root mean square error values and closely aligning with experimental data. Moreover, comparative analysis with other recent optimization approaches highlights the RSALF optimizer's superiority, making it a promising tool for advancing the optimization of PV models and facilitating more efficient and sustainable solar energy utilization.

Intelligent algorithm-based analysis of corporate financial decisions in the era of cloud accounting

Abstract The development of cloud computing technology has promoted a new information technology revolution, and the arrival of the cloud accounting era has brought new ideas to enterprise financial decision-making. This paper first introduces the improved swarm intelligence algorithm grasshopper optimization algorithm optimization, using reverse learning the Levy flight mechanism to improve the global search ability of the original grasshopper algorithm, using the Gaussian variation mechanism to increase the diversity of the population to achieve algorithm optimization. The IGOA algorithm is then used to optimize the internal parameters of KELM by dynamically adjusting the optimal parameters based on the training set data through a 5-fold cross-validation analysis to evaluate the external classification performance. Then the factors affecting the financial decision analysis and the financial decision process in the cloud accounting era are introduced, and finally, some problems in traditional enterprise decision methods and the impact of intelligent algorithms on enterprise financial decisions are analyzed. In terms of the existing problems, the main ones are: 83% of them think that the traditional enterprise decision-making methods need to be innovated, and 83% of them think there is a shortage of professionals with professional financial decision-making expertise. On the impact of intelligent algorithms on enterprise financial decision-making, 88% think that the decision analysis dimension has been broadened based on intelligent algorithms, and 85% think that the decision risk has achieved fine control. The development and application of intelligent algorithms and cloud accounting technology have an important and positive effect on the financial decision-making mode of enterprises.

Two level blockchain-based privacy preservation framework in IoT with heuristic fusion mechanism-aided deep learning architecture

The Internet of Things (IoT) connects more objects used in the modern world. To connect IoT nodes, it needs maintenance services, robustness, frictionless authentication, and high security. Blockchain emerges as a workable solution by providing those important features. Several authentication problems, maintenance, and security with IoT systems are solved because of blockchain technology. The blockchain-based IoT network is an open source, and everyone may see encrypted keys and transactional information. So, from this public network, anyone can take crucial information about users. A new blockchain-adopted IoT-based privacy preservation approach is developed to provide higher security with less utilization of the cost requirements. Here, two levels of data privacy is accomplished by utilizing the deep learning approach. The first level of privacy is used for authenticating a person, where the authentication among collected data is performed using Modified Adaboost and Long Short-Term Memory (MABLSTM), and the authenticated data is stored in the blockchain database. After that, the second level of privacy is obtained by performing data encoding with the encoder part of Autoencoder, and further Elliptic Curve Cryptography (ECC) is used to encrypt the data. Hence, the encrypted data is given to the data decryption process, and this has been subjected to the decoder part of the autoencoder to reconstruct the original data. Moreover, the parameters in the autoencoder and the parameters within the MABLSTM are optimized with the help of the developed Modified Levy Flight Distribution with Grasshopper Optimization (MLFD-GO). However, the parameters like LSTM and Adaboost are optimized with the help of the MLFD-GO algorithm to enhance the performance in terms of accuracy and precision for providing the higher level authentication scheme to provide high level security. Finally, the performance of the developed blockchain-based privacy preservation approach is validated over recently developed techniques.

Adversarial transformer network for classification of lung cancer disease from CT scan images

Lung cancer is a dreadful disease that affects both men and women; an early prognosis is highly required to prolong human life. Recently, the computer-aided diagnosis (CAD) system has played a vital role in effectively diagnosing lung-related diseases. However, this system often fails to identify the type of cancers and is subjected to high error and computation complexity. Hence, this article introduces a novel deep learning (DL) technique to classify different types of lung cancers using chest CT images. The proposed study undertakes three major stages: pre-processing, feature selection and classification. The guided bilateral filtering (GWF) technique is initially introduced in the pre-processing stage to eliminate the noises from the raw CT images. Then, dimensionality reduction is performed using the weighted least absolute shrinkage and selection vector regression (We-LASSVR) technique to avoid over-fitting issues in the network model. In addition, the weighted mean of vector optimization (We-WMVO) technique is introduced to develop the performance of a proposed We-LASSVR technique. Finally, the transformer-aided generative adversarial network (T-GAN) technique is proposed to effectively classify the different types of lung cancers. Moreover, the dynamic levy flight chimp optimization (DyLF-CO) technique is proposed to tune the network model. The proposed model is employed in Python, and a chest CT image from the Kaggle website is collected and processed in this study. In the experimental section, the proposed method obtains an accuracy of 0.997, precision of 0.996, specificity of 0.998, RMSE of 0.104 and time complexity of 120 s.

Production analytics for renewable energy portfolio management: A comparative study of active and passive strategies in digital twin

Developed societies are facing a number of problems due to technological advances, including the increased requirement for power, resulting in small electrical grids utilizing local power sources, also known as a microgrid (MG). The MG is linked to the central electrical network or runs alone in the form of a power island based on the renewable energy sources and dispatchable units. On the other hand, the smart grid concept is built based on the employment of sensors and cloud computing systems to address the problems. It is possible to decrease energy consumption problems by using a smart MG; however, proper implementation is necessary to avoid generating too much information. As a result, the study uses fog computing for energy production and consumption analysis and management by reducing the amount of information passing to the cloud. The proposed model would reduce service response times in an active strategy. To this end, an enhanced particle swarm optimization with Levy Walk is used for solving the optimization problem based on the real-time data receiving from the digital twin of the system. As a result, to evaluate the efficiency and accuracy of the fog in the case, efficiency evaluations based on active and passive strategies are carried out in order to determine whether parameters including the number of residents, the optimization algorithm, moving appliances, and the power resource affect response times and source demands.

Rhizostoma optimization algorithm and its application in different real-world optimization problems

&lt;p&gt;In last decade, numerous meta-heuristic algorithms have been proposed for dealing the complexity and difficulty of numerical optimization problems in the realworld which is growing continuously recently, but only a few algorithms have caught researchers’ attention. In this study, a new swarm-based meta-heuristic algorithm called Rhizostoma optimization algorithm (ROA) is proposed for solving the optimization problems based on simulating the social movement of Rhizostoma octopus (barrel jellyfish) in the ocean. ROA is intended to mitigate the two optimization problems of trapping in local optima and slow convergence. ROA is proposed with three different movement strategies (simulated annealing (SA), fast simulated annealing (FSA), and Levy walk (LW)) and tested with 23 standard mathematical benchmark functions, two classical engineering problems, and various real-world datasets including three widely used datasets to predict the students’ performance. Comparing the ROA algorithm with the latest meta-heuristic optimization algorithms and a recent published research proves that ROA is a very competitive algorithm with a high ability in optimization performance with respect to local optima avoidance, the speed of convergence and the exploration/exploitation balance rate, as it is effectively applicable for performing optimization tasks.&lt;/p&gt;

Drone Powered by Solar Energy and Whale Optimization Algorithm

In this paper, the whale optimization algorithm (WOA), a potent swarm intelligence technique that has been widely applied in numerous sectors, including the parameter identification of solar cells and PV modules, is discussed in this work. A unique modified WOA (MWOA) that introduces both a mutation method based on Levy flight and a local search mechanism of pattern search to better balance the exploration and exploitation of WOA. On the one hand, Levy flight can force the algorithm to abandon the local optimum and avoid stagnation; as a result, it can stop the algorithm from losing diversity and improve the algorithm's ability to conduct global searches. However, pattern search, a direct search technique, has a high convergence rate and good stability, which can increase local search efficiency. Therefore, the ability of WOA to find the optimal solution can be significantly increased by combining these two techniques. Additionally, MWOA may be used to determine the unknown parameters of two different types of PV modules under various light irradiance and temperature circumstances, as well as to estimate parameters in the single diode model (SDM), double diode model (DDM), and PV modules.

A multi-strategy improved sparrow search algorithm of large-scale refrigeration system: Optimal loading distribution of chillers

At present, the energy consumption of the parallel chillers system accounts for about 60% of the whole energy consumption of central air conditioning system and 25– 40% of the total energy consumption. Unreasonable load distribution brings huge energy consumption and carbon emissions during building operation. In order to reduce energy consumption and carbon emissions of the parallel chillers system in large-scale refrigeration system, we proposed an improved sparrow search algorithm (TMSSA). This algorithm initializes the population using Tent chaotic map to increase population diversity, which can enhance the possibility of TMSSA to obtain the optimal solution before the optimization iteration, and speed up the convergence process. Besides, a Levy flight mechanism is applied to improve the position update of the producer, enhancing the randomness and local search ability of this algorithm. Moreover, Gaussian mutation method is utilized to perturb the position of scroungers, strengthening the ability of the algorithm to escape local optima to improve robustness of TMSSA. To evaluate the proposed algorithm, 12 benchmark functions were chosen, and the results showed that it overcomes the limitations of traditional SSA regarding local optima trapping. The algorithm is also used to solve the optimal chiller loading (OCL) problem, which has 3 typical cases, and comparisons were made with other algorithms. The results further demonstrate that TMSSA is highly accurate, fast-converging, and robust with excellent energy-saving performance.

Modeling and IAHA Solution for Task Scheduling Problem of Processing Crowdsourcing in the Context of Social Manufacturing

The paper addresses the discrete characteristics of the processing crowdsourcing task scheduling problem in the context of social manufacturing, divides it into two subproblems of social manufacturing unit selecting and subtask sorting, establishes its mixed-integer programming with the objective of minimizing the maximum completion time, and proposes an improved artificial hummingbird algorithm (IAHA) for solving it. The IAHA uses initialization rules of global selection, local selection, and random selection to improve the quality of the initial population, the Levy flight to improve guided foraging and territorial foraging, the simplex search strategy to improve migration foraging to enhance the merit-seeking ability, and the greedy decoding method to improve the quality of the solution and reduce solution time. For the IAHA, orthogonal tests are designed to obtain the optimal combination of parameters, and comparative tests are made with variants of the AHA and other algorithms on the benchmark case and a simulated crowdsourcing case. The experimental results show that the IAHA can obtain superior solutions in many cases with economy and effectiveness.

Evolving deep gated recurrent unit using improved marine predator algorithm for profit prediction based on financial accounting information system

This research proposes a hybrid improved marine predator algorithm (IMPA) and deep gated recurrent unit (DGRU) model for profit prediction in financial accounting information systems (FAIS). The study addresses the challenge of real-time processing performance caused by the increasing complexity of hybrid networks due to the growing size of datasets. To enable effective comparison, a new dataset is created using 15 input parameters from the original Chinese stock market Kaggle dataset. Additionally, five DGRU-based models are developed, including chaotic MPA (CMPA) and the nonlinear MPA (NMPA), as well as the best Levy-based variants, such as the dynamic Levy flight chimp optimization algorithm (DLFCHOA) and the Levy-base gray wolf optimization algorithm (LGWO). The results indicate that the most accurate model for profit forecasting among the tested algorithms is DGRU-IMPA, followed by DGRU-NMPA, DGRU-LGWO, DGRU-DLFCHOA, DGRU-CMPA, and traditional DGRU. The findings highlight the potential of the proposed hybrid model to improve profit prediction accuracy in FAIS, leading to enhanced decision-making and financial management.

IoT Resource Allocation and Optimization Using Improved Reptile Search Algorithm

The Internet of Things (IoT) is a dispersed network system that connects the world through the Internet. The architecture of IoT consists of more gateways and resources which cannot be allocated in a manual process. The allocation of resources in IoT is a challenging process due to the higher consumption of energy and high latency rate. To overcome the challenges in existing works, this research introduced an Improved Reptile Search Algorithm (IRSA) to solve the optimization problem which occurs during the time of allocation resources among IoT networks. IRSA employs the methodology of levy flight and cross-over to update the candidate position and enhance the search speed in a single iteration. The proposed method consumes less energy and has low latency during data transmission from User equipment (UE) to the base station.IRSA has been compared with the existing Scalable Resource Allocation Framework (SRAF) and Improved Chaotic Firefly Algorithm (ICFA). The obtained experimental results show that the proposed IRSA attained better performance with an allocation rate of 96.40% which is comparatively higher than SRAF and ICFA with 92.40% and 91.67% respectively.