Harris Hawks Optimization Algorithm Research Articles

Research on SAR image quality evaluation method based on improved harris hawk optimization algorithm and XGBoost

Research on SAR image quality evaluation method based on improved harris hawk optimization algorithm and XGBoost

Seismic Damage Identification Using a Combination of Mixture of Gaussian and Harris Hawks Optimization

ABSTRACTThe present study aims to identify damage in structures using seismic responses and a two‐stage method based on mixture of Gaussian (MOG) and Harris hawks optimization (HHO). Two‐dimensional (2‐D) frame structures under seismic loads are simulated using the finite element method (FEM). The placement of sensors is optimized using a combination of an iterated improved reduced system (IIRS) and the binary differential evolution (BDE) algorithm. The MOG classifier is trained to find the damaged story and damaged element type (i.e., whether it is a beam or a column) using the nodal acceleration responses at the optimal sensor placement of the seismically loaded structure. Thus, as the first step, the possibly damaged elements are located through the classifier. Then, in the second step, the damage is accurately located and quantified by HHO algorithm. The performance of the proposed method is assessed using the numerical results of 2‐D frames of 7 and 14 stories in different damage scenarios with and without considering noise. As a result, the efficiency of the proposed method for the seismic damage identification of 2‐D frames is revealed. Sensor positions are well optimized, and it causes the method to be highly effective. Moreover, MOG correctly finds a damaged story and identifies whether a damaged element is a beam or a column. Damage in the presence of noise is also localized and quantified precisely by HHO.

Research on Resource Allocation Algorithm for Non-Orthogonal Multiple Access Visible Light Communication

In order to satisfy the large-scale access of visible light communication (VLC) users, as well as the demand for user request rate, the resource allocation problem of visible light communication with drone-assisted non-orthogonal multiple access (NOMA) technique is investigated. An efficient scheme for joint optimization of power allocation and access point (AP) location is proposed. According to the state of user channel information, a user pairing strategy with uniform channel gain difference for any number of users is designed, and an objective function of maximizing the average user data rate with constraints is constructed. For this non-convex NP-hard problem, the optimization problem with constraints is transformed into an optimization problem without constraints by introducing the idea of a penalty function and then solved by the Harris Hawk Optimization (HHO) algorithm based on the nonlinear energy convergence factor, and ultimately, the optimal user power allocation factor, as well as the location of the AP, are found. The simulation results show that the scheme in this paper can improve the average user data rate better compared to other classical schemes. The system performance of the HHO algorithm is improved by about 20.31% compared to the Particle Swarm Optimization (PSO) algorithm. The HHO algorithm based on the nonlinear energy convergence factor improves the convergence speed by about 50% compared to the classical HHO algorithm.

Development of Multi-Objective Harris Hawks Optimization (MOHHO) Algorithm in Low-Impact Development Systems Considering the Effects of Climate Change

Development of Multi-Objective Harris Hawks Optimization (MOHHO) Algorithm in Low-Impact Development Systems Considering the Effects of Climate Change

OB-TSASA Guided Soft Voting Ensemble Classifier for Breast Cancer Classification Using Thermal Infrared Images

Nowadays, the use of thermal infrared images has become more popular in breast cancer detection. Thermal image analysis with machine learning algorithms allows physicians to make accurate decisions on cancer diagnostics. Hence, this paper introduces a novel automated breast cancer classifier using thermal infrared images. Initially, the features from the DMR – IR dataset are extracted by an integrated approach of Kernel Principle Fast Independent-Component Analysis (KPFICA) and deep object-centric pooling convolution neural network (DOCP-CNN). Additionally, the DOCP-CNN hyperparameters are tuned by the Harris Hawks Optimization (HHO) algorithm. Next, the proposed oppositional based tunicate swarm algorithm with simulated annealing (OB-TSASA) optimization is used to select the relevant features from the extracted features. Finally, the OB-TSASA guided soft voting ensemble classifier is proposed to detect and classify cancerous patients. The experiment is conducted on the DMR – IR dataset to estimate the effectiveness of the proposed method. Furthermore, the proposed method produces outstanding classification results in terms of AUC, accuracy, F1-score, sensitivity, MCC, and kappa statistics. It is observed that the classification accuracy is 97.73% which is superior to other classifiers and the error rate is 2.27% which is very low compared to other methods.

A Maximum Power Point Tracking (MPPT) Strategy Based on Harris Hawk Optimization (HHO) Algorithm

To address the multi-peak characteristics of the P-U output characteristic curve when the photovoltaic array is partially shaded, a Maximum Power Point Tracking (MPPT) method combining Harris Hawk optimization, and a variable step conductance increment method is proposed in this article. Although the Harris Hawk Optimization algorithm has advantages in optimizing multi-modal P-U curves, when the lighting conditions suddenly change, the output characteristic curve will undergo drastic changes, causing the algorithm to oscillate repeatedly at the maximum power point. Moreover, due to its inherent limitations, the Harris Hawk algorithm has a low convergence accuracy, slow tracking speed, and is prone to getting trapped in a local optimum. In order to compensate for the shortcomings of the Harris Hawk Optimization algorithm, the variable step conductance increment method is switched for local exploration to improve the algorithm’s ability to escape from the local optimal solution and reduce power oscillation. Finally, the MPPT control of the proposed strategy was simulated and verified on the MATLAB/Simulink simulation (2021) platform. In the MPPT test experiment, the proposed composite algorithm was applied to simulate and verify uneven irradiance conditions.

Adaptive Variable Universe Fuzzy Droop Control Based on a Novel Multi-Strategy Harris Hawk Optimization Algorithm for a Direct Current Microgrid with Hybrid Energy Storage

In the off-grid photovoltaic DC microgrid, traditional droop control encounters challenges in effectively adjusting the droop coefficient in response to varying power fluctuation frequencies, which can be influenced by factors such as line impedance. This paper introduces a novel Multi-strategy Harris Hawk Optimization Algorithm (MHHO) that integrates variable universe fuzzy control theory with droop control to develop an adaptive variable universe fuzzy droop control strategy. The algorithm employs Fuch mapping to evenly distribute the initial population across the solution space and incorporates logarithmic spiral and improved adaptive weight strategies during both the exploration and exploitation phases, enhancing its ability to escape local optima. A comparative analysis against five classical meta-heuristic algorithms on the CEC2017 benchmarks demonstrates the superior performance of the proposed algorithm. Ultimately, the adaptive variable universe fuzzy droop control based on MHHO dynamically optimizes the droop coefficient to mitigate the negative impact of internal system factors and achieve a balanced power distribution between the battery and super-capacitor in the DC microgrid. Through MATLAB/Simulink simulations, it is demonstrated that the proposed adaptive variable universe fuzzy droop control strategy based on MHHO can limit the fluctuation range of bus voltage within ±0.75%, enhance the robustness and stability of the system, and optimize the charge and discharge performance of the energy storage unit.

Learning Harris Hawk Algorithm Based on Signal-to-Noise Ratio

In view of the problem of insufficient population learning and adaptability of the Harris Hawk optimization algorithm, this paper proposes a learning Harris Hawk algorithm based on signal-to-noise ratio, referred to as SLHHO. This algorithm introduces the concept of signal-to-noise ratio to determine the location information of individuals, and designs a coordinated learning strategy that can more reasonably update the location of individuals within the population, and then redesign the escape distance to improve the adaptation and optimization of the algorithm. ability. Using 12 benchmark functions as standards, the performance of this algorithm was tested with variants of the Harris Eagle algorithm and other algorithms, and comparative analysis was conducted in evaluation indicators such as time complexity, diversity, exploration and development, and the results show that SLHHO has strong competitiveness and feasibility. Finally, the practicality of SLHHO was verified in the pressure vessel design problem.

Innovative application of transfer learning on small-scale datasets: Analysis and optimization of catalyst ink for the low-iridium membrane electrode assemblies of proton exchange membrane water electrolysis

The scarcity of Ir poses a significant barrier to the widespread adoption of proton exchange membrane water electrolysis (PEMWE), necessitating minimization of Ir loading in membrane electrode assemblies (MEAs). This study introduced a transfer learning strategy to optimize catalyst ink formulations for performance enhancement of low-Ir MEAs. We leveraged a pre-trained model from previous research and fine-tuned it on a small dataset of 12 MEAs’ ink preparation data, which significantly reduced labor, time, and costs associated with extensive experimentation. The developed model demonstrated high accuracy with an R2 of 0.99507. The parameters of the ink formulation were assessed using the Shapley Additive exPlanations method and optimized through the Harris Hawk Optimization algorithm. This optimization yielded an electrolysis voltage of 1.898 V at 3.0 A cm−2 for MEAs with 0.15 mg cm−2 Ir loading. These performance improvements are attributed to enhanced catalyst-ionomer particle dispersion and optimized rheological properties of the ink, which lead to more uniform distribution of Ir in the catalytic layers. These results demonstrate the feasibility and efficacy of transfer learning techniques to develop high-performance models from limited datasets, offering a promising pathway for accelerated materials discovery and optimization in the field of PEMWE.

EBH-IoT: Energy-efficient secured data collection and distribution of electronics health record for cloud assisted blockchain enabled IoT based healthcare system

The integration of Health IoT (H-IoT) and blockchain technologies are being heavily exploited and used in many domains, especially for e-healthcare to collect the data i.e electronic health record (EHR) from the patient. The H-IoT devices have the ability to provide real-time sensory data from patients to be processed and analyzed, and distributed. Blockchain is providing decentralized computation, distribution and storage for EHR data. Therefore, the integration of H-IoT and Blockchain technologies can become a reasonable choice for the design of decentralized H-IoT-based e-healthcare systems. But the H-IoT network has some intrinsic challenges like low computation, energy constraint, security, energy optimization, data storage, and real-time data analytic. Also, conventional EHR-based systems suffer from issues such as the potential loss of data, inadequate security and consensus on the unchangeable nature of health records, fragmented connections between different institutions, and ineffective clinical data retrieval methods, among other challenges. In this article, first, we study the performance of blockchain technology in the healthcare system. Second, we propose an improved Harris Hawk Optimization algorithm (HHO) based clustering mechanism for the collection and sharing of EHR. The proposed system was tested and validated using the Hyperledger-fabric based electronic healthcare record (EHR) sharing system along with Matlab. The proposed system achieves 12%, and 7% incremental improvement in terms of latency, throughput for the Blockchain networks. While the proposed clustering technique achieves 10%, 12%, 14%, and 16% improvements in alive node, energy consumption, throughput and average transmission delay compared to existing state of the art.

Classifying fabric wrinkle via regularized random vector function link optimized by African vultures optimization algorithm

The wrinkling of fabrics not only affects their aesthetic appeal and comfort but also leads to increased wear and a reduced lifespan. Consequently, wrinkle resistance has become a critical metric in evaluating fabric performance. Traditional wrinkle grading methods rely heavily on expert assessments, which are susceptible to visual fatigue, resulting in subjective and inconsistent classification outcomes. To address this issue, this article proposes an automatic wrinkle grading model based on an improved African Vultures Optimization (AVO) algorithm, which optimizes a regularized random vector functional link (RRVFL) network. The model enhances convergence speed and classification accuracy by incorporating the Harris Hawks Optimization (HHO) algorithm to provide a superior initial population for the AVO algorithm. Additionally, the AVO algorithm’s local search capability is strengthened during the optimization process, improving the model’s stability and resistance to overfitting. Experimental results demonstrate that the proposed HHO-AVO-RRVFL model achieves an average classification accuracy of 97.86% across multiple datasets, significantly outperforming other comparative algorithms while also excelling in classification stability and convergence speed.

Optimization of biomass-fueled multigeneration system using SOFC for electricity, hydrogen, and freshwater production

The rapid depletion of fossil fuel resources and their detrimental impact on the environment necessitates the exploration of renewable energy alternatives. Biomass stands out as a reliable renewable source, especially in multigeneration systems. This study suggests a biomass-driven multigeneration configuration for the production of electricity, heating, hydrogen, and freshwater. The configuration is evaluated from thermodynamic and economic perspectives, utilizing an Artificial Neural Network to predict key outcomes. The system optimization process integrates Machine Learning with decision-making methods to achieve optimal conditions. The findings highlight that an anode-cathode gas recycling design is the most effective configuration for the Solid Oxide Fuel Cell unit, achieving a cycle efficiency of 67.68% and a product cost of $11.41/GJ. Municipal Solid Waste biomass and CO2 were identified as the most efficient fuel and gasification agents, respectively. The evaluations also determined that the integration of the multi-objective grey wolf optimizer and the CatBoost method is the most reliable machine learning model for optimization. Furthermore, by combining the Multi-Objective Harris Hawks Optimization algorithm with TOPSIS, LINMAP, and Fuzzy decision-making approaches, the system achieves optimal performance across multiple scenarios. In one scenario, the system reached an exergy efficiency of 67.68% and a hydrogen production rate of 22.34 kg/h. The optimum recycling ratios for the anode and cathode were determined to be 0.16 and 0.38, respectively. The study demonstrates the feasibility of the proposed configuration, offering significant potential for sustainable and cost-effective energy generation.

Multi-Strategy Improved Harris Hawk Optimization Algorithm and Its Application in Path Planning.

Path planning is a key problem in the autonomous navigation of mobile robots and a research hotspot in the field of robotics. Harris Hawk Optimization (HHO) faces challenges such as low solution accuracy and a slow convergence speed, and it easy falls into local optimization in path planning applications. For this reason, this paper proposes a Multi-strategy Improved Harris Hawk Optimization (MIHHO) algorithm. First, the double adaptive weight strategy is used to enhance the search capability of the algorithm to significantly improve the convergence accuracy and speed of path planning; second, the Dimension Learning-based Hunting (DLH) search strategy is introduced to effectively balance exploration and exploitation while maintaining the diversity of the population; and then, Position update strategy based on Dung Beetle Optimizer algorithm is proposed to reduce the algorithm's possibility of falling into local optimal solutions during path planning. The experimental results of the comparison of the test functions show that the MIHHO algorithm is ranked first in terms of performance, with significant improvements in optimization seeking ability, convergence speed, and stability. Finally, MIHHO is applied to robot path planning, and the test results show that in four environments with different complexities and scales, the average path lengths of MIHHO are improved by 1.99%, 14.45%, 4.52%, and 9.19% compared to HHO, respectively. These results indicate that MIHHO has significant performance advantages in path planning tasks and helps to improve the path planning efficiency and accuracy of mobile robots.

An optimized isolation forest based intrusion detection system for heterogeneous and streaming data in the industrial Internet of Things (IIoT) networks

While conventional Intrusion Detection Systems (IDS) are essential for defending against intruders in the Industrial Internet of Things (IIoT), handling data from heterogeneous and streaming data sources should receive more attention. This work introduces a novel Optimized IForest-based Intrusion Detection System (OIFIDS) which is designed to handle both heterogeneous and streaming data efficiently. The suggested approach employs a collection of optimized binary trees, each of which is trained on a distinctive subset of data, and in which the location of empty leaves determines the anomaly score assigned to a certain data point. Optimizing isolation Forest (iForest) utilizing a modified version of the Harris Hawks Optimization algorithm, which exploits both Exploration factor and Random walk strategies (ERHHO) decreases the dataset's dimension, decreases its learning time, and enhances the detection precision, accuracy, F1-score, FPR, and recall. To demonstrate how effective the proposed approach is, it is evaluated using three datasets: CICIDS-2018, NSL-KDD, and UNSW-NB15. The experimental results prove the ability of the suggested approach in handling both heterogeneous and streaming data efficiently and delivering results that were comparable to the cutting-edge baseline techniques. Moreover, it performs effectively when there are no anomalies in the training sample and when dealing with challenging scenarios with several irrelevant features and high dimensions. Based on the comparison with various state-of-the-art IDSs, the suggested approach is able to detect intrusion with greater accuracies of 95.6%, 94.8%, and 99% than the other approaches on the NSL-KDD, UNSW-NB15, and CICIDS-2018 datasets, respectively. Experiments on heterogenous data reveal that Area Under the ROC Curve (AUC) of OIFIDS beats the baseline approach for UNSW-NB15 dataset and is higher than the second-best method by 8% and 2.4% for NSL-KDD and CICIDS-2018 respectively. Evaluating the proposed system on streaming data illustrates that it can address the concept drift problem well with high AUC value of 0.948, 0.97, and 0.922 on the NSL-KDD, CICIDS-2018, and UNSW-NB15 datasets, respectively.

Melanoma skin cancer detection based on deep learning methods and binary Harris Hawk optimization

AbstractThe issue of skin cancer has garnered significant attention from the scientific community worldwide, with melanoma being the most lethal and uncommon form of the disease. Melanoma occurs due to the uncontrolled growth of melanocyte cells, which are responsible for imparting color to the skin. If left untreated, melanoma can spread throughout the body and cause death. Early detection of melanoma can lower its mortality rate. In this study, we propose a robust Convolutional Neural Network (CNN)-based method for classifying melanoma images as healthy or non-healthy. To train and test the model, we utilized public datasets from International Skin Imaging Collaboration (ISIC). Additionally, we compared our method with other classification techniques, including Support Vector Machine (SVM), Decision Tree, and K-Nearest Neighbors (K-NN), using the Harris Hawks Optimization algorithm. The results of our method showed superior performance compared to the other approaches.

Multi-objective Harris Hawks optimization algorithm for selecting best location and size of distributed generation in radial distribution system

Distributed Generating Units (DGUs) are widely used in backup power, renewable energy integration, voltage regulation, and energy storage applications. The DGUs are small-scale power-generating units located near the load centers of a power system, as opposed to large, centralized power plants located far away. However, the DGUs must reduce power losses and environmental impacts and improve the voltage profile. This was achieved by effective optimization of the position and rating of DGU. However, the conventional optimization methods failed to optimize the power losses, voltage-current balancing issues, and harmonic distortions. So, this article presents the selection of the best location and size of DGU in radial distribution systems using a nature-inspired multi-objective Harris Hawks optimization (HHO) algorithm while considering improvements in the voltage profile and reductions in active power losses. The HHO algorithm is inspired by the supportive actions of intelligent birds, such as Harris Hawks, while searching for prey. The distinguished hunting process of Harris Hawks using other family members makes it unique, which is useful for searching for better quality solutions while optimizing multi-objective fitness functions. Fitness function is determined by considering each bus's voltage profile and branches' active power losses. The proposed method is tested on two circle distribution systems (69 bus and 118 bus). The results are compared to those obtained using some of the more well-known optimization techniques given by scholars in recent years. The optimization of seven DGU in 118 bus radial distribution systems resulted in 405.32 kW of active power losses, a 68.78% decrease in losses, and 0.9723 Vs of minimum voltage.

Modified Harris Hawks optimization for the 3E feasibility assessment of a hybrid renewable energy system

The off-grid Hybrid Renewable Energy Systems (HRES) demonstrate great potential to be sustainable and economically feasible options to meet the growing energy needs and counter the depletion of conventional energy sources. Therefore, it is crucial to optimize the size of HRES components to assess system cost and dependability. This paper presents the optimal sizing of HRES to provide a very cost-effective and efficient solution for supplying power to a rural region. This study develops a PV-Wind-Battery-DG system with an objective of 3E analysis which includes Energy, Economic, and Environmental CO2 emissions. Indispensable parameters like technical parameters (Loss of Power Supply Probability, Renewable factor, PV fraction, and Wind fraction) and social factor (Human Developing Index) are evaluated to show the proposed modified Harris Hawks Optimization (mHHO) algorithm’s merits over the existing algorithms. To achieve the objectives, the proposed mHHO algorithm uses nine distinct operators to obtain simultaneous optimization. Furthermore, the performance of mHHO is evaluated by using the CEC 2019 test suite and the most optimal mHHO is chosen for sizing and 3E analysis of HRES. The findings demonstrate that the mHHO has achieved optimized values for Cost of Energy (COE), Net Present Cost (NPC), and Annualized System Cost (ASC) with the lowest values being 0.14130 $/kWh, 1,649,900$, and 1,16,090$/year respectively. The reduction in COE value using the proposed mHHO approach is 0.49% in comparison with most of the other MH-algorithms. Additionally, the system primarily relies on renewable sources, with diesel usage accounting for only 0.03% of power generation. Overall, this study effectively addresses the challenge of performing a 3E analysis with mHHO algorithm which exhibits excellent convergence and is capable of producing high-quality outcomes in the design of HRES. The mHHO algorithm attains optimal economic efficiency while simultaneously minimizing the impact on the environment and maintaining a high human development index.

Behavioral analysis of electricity consumption characteristics for customer groups using the k-means algorithm

In the fierce competition of the electricity market, how to consolidate and develop customers is particularly important. Aiming to analyze the electricity consumption characteristics of customer groups, this paper used a k-means algorithm and optimized it. The number of clusters was determined by the Davies-Bouldin index (DBI). An improved Harris Hawks optimization (IHHO) algorithm was designed to realize the initial cluster center selection. Based on data such as electricity purchase and average electricity price, electricity customer groups were clustered using the IHHO-k-means algorithm. The IHHO-k-means algorithm achieved the best clustering effect on Iris, Wine, and Glass datasets compared with the traditional k-means and PSO-k-means algorithms. Taking Iris as an example, the optimal value of the IHHO-k-means algorithm was 96.538, with an accuracy rate of 0.932, precision and recall rates of 0.941 and 0.793, respectively, an F-measure of 0.861, and an area under the curve (AUC) value of 0.851. In the customer dataset, the number of clusters determined by DBI was 4. The power customers were divided into four groups with different characteristics of electricity consumption, and their electricity consumption behaviors were analyzed. The results prove the reliability of the IHHO-k-means algorithm in analyzing electricity consumption characteristics of customer groups, and it can be applied in practice.

Exploring the machining characteristics of aluminum 6061 using nanosecond pulse fiber laser machine

The present study aims to probe the influence of nanosecond laser parameters on the surface quality of aluminum material (Al 6061) using a full factorial design approach. Additionally, the study utilizes the Harris Hawks optimization (HHO) algorithm to determine the optimized laser parameters for achieving desirable surface features on straight-cut aluminum samples. Subsequently, the machined samples are analyzed through optical microscopy, scanning electron microscopy (SEM), and energy dispersive spectroscopy (EDS). The evaluation criteria for the present study are taper, surface roughness, and heat-affected zone (HAZ) thickness. The results revealed that average laser power significantly impacted the taper (17.01%), and scanning speed contributed significantly to the taper (26.62%). The average power and scanning speed combined showed the most substantial influence on taper (47.76%). Furthermore, the average power had the most significant effect on the heat-affected zone (77.76%) and surface roughness (SR) (72.22%). The optimal conditions determined by the HHO were a pulse frequency of 100 Hz, a scanning speed of 10 mm/s, and an average power of 40 W, resulting in heat-affected zone = 36.047, surface roughness = 5.496, and taper angle = 23.188. These findings hold significant implications for enhancing the surface characteristics of aluminum in laser machining processes, thereby benefiting industries such as aerospace, automotive, and electronics.

Evaluating machine learning models in predicting dam inflow and hydroelectric power production in multi-purpose dams (case study: Mahabad Dam, Iran)

This study aimed to forecast dam inflows and subsequently predict its capability in producing HEPP using machine learning and evolutionary optimization techniques. Mahabad Dam, located in the northwest of Iran and recognized as one of the nation’s key dams, served as a case study. First, artificial neural networks (ANN) and support vector regression (SVR) were employed to predict dam inflows, with optimization of parameters achieved through Harris hawks optimization (HHO), a robust optimization technique. The data of temperature, precipitation, and dam inflow over a 24-year period on a monthly basis, incorporating various lag times, were used to train these machines. Then, HEPP from the dam was predicted using temperature, precipitation, dam inflow, and dam evaporation as input variables. The models were applied to data covering the years 2000 to 2020. The results of the first part indicated both hybrid models (HHO-ANFIS and HHO-SVR) improved the prediction performance compared to the single models. Based on the results of Taylor’s diagram and the error evaluation criteria, the HHO-ANFIS hybrid model (RMSE, MAE, and NSE of 3.90, 2.41, and 0.86, respectively) exerted better performance than HHO-SVR (RMSE, MAE, and NSE of 4.39, 2.70, and 0.86, respectively). The results of the second part showed that using the HHO algorithm to optimize single models (RMSE, MAE, and NSE of 0.2, 10, and 0.90, respectively) predicted HEPP better than single models (RMSE, MAE, and NSE of 0.2, 10, and 0.90, respectively). The results of Taylor’s diagram also showed that the HHO-ANFIS model exerted better performance. The findings of this study indicated the promising performance of machine learning models optimized by metaheuristic algorithms in the simultaneous prediction of dam inflows and HEPP in multi-purpose dams for better management and allocation of surface water resources.