Harris Hawks Optimization Research Articles

Opposition-based learning Harris hawks optimization with steepest convergence for engineering design problems

Opposition-based learning Harris hawks optimization with steepest convergence for engineering design problems

LEVERAGING CORPUS LINGUISTICS AND DATA-DRIVEN DEEP LEARNING FOR TEXTUAL EMOTION ANALYSIS

Emotions have played a major part in the conversation, as they express context to the conversation. Text or words in conversation contain contextual and lexical meanings. In recent times, obtaining emotion from the text has been an attractive area of research. With the emergence of machine learning (ML) algorithms and hardware to aid the ML method, identifying emotion from the text with ML provides significant and promising solutions. The main objective of Textual Emotion Analysis (TEA) is to analyze and extract the user’s emotional states in the text. Many different Complex Systems and Deep Learning (DL) algorithms have been fast-paced developed and proved their effectiveness in several fields including audio, image, and natural language processing (NLP). This has moved researchers away from the classical ML to DL for their academic research work. This study develops a new Corpus Linguistics and Data-Driven Deep Learning for Textual Emotion Analysis (CLD3L-TEA) technique. The CLD3L-TEA technique mainly investigates the distinct types of emotions that endure in the social media text. In the CLD3L-TEA model, the raw data can be pre-processed in distinct ways. Next, a multi-weighted TF–IDF model is used to generate feature vectors. For the identification of emotions, the CLD3L-TEA technique applied a gated recurrent unit (GRU). At last, the hyperparameter range of the GRU model is executed by the Fractal Harris Hawks Optimization (HHO) model. The experimental validation of the CLD3L-TEA technique on a benchmark dataset illustrates the supremacy of this technique over recent approaches.

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.

Efficient Flood Detection through Hybrid Machine Learning and Metaheuristic Methods using Sentinel-1

Abstract. Floods are considered among the most destructive natural disasters, requiring precise and timely management. Remote sensing, utilizing diverse satellite imagery data, enables effective monitoring and assessment of flood impacts. In this context, machine learning and deep learning methods, as effective and scalable approaches, can significantly enhance the accuracy of flood detection and management by analyzing remote sensing data, thereby playing a crucial role in mitigating flood-related risks. In this study, to flood detection using Sentinel-1 SAR data, machine learning algorithms, including Random Forest (RF) and Histogram-based Gradient Boosting Decision Tree, were employed, along with two metaheuristic algorithms, Harris Hawks Optimization (HHO) and Ant Colony Optimization (ACO), for hyperparameter optimization. Additionally, to enhance the models' ability to detect flooded pixels and improve overall performance accurately, a pre-trained VGG-16 Neural Network was used as a deep feature extractor. Finally, four ensemble flood detection models—RF-HHO, RF-ACO, HGBDT-HHO, and HGBDT-ACO—were implemented, and their performance was evaluated and compared based on statistical metrics. Based on the obtained results, all four ensemble flood detection models demonstrated excellent performance in the validation and testing phases. The overall accuracy of these models reached over 95% in the validation phase and exceeded 97% in the testing phase. However, the HGBDT-ACO model achieved the highest accuracy and the lowest error rate in detecting flood pixels, making it the best-performing model in this study. Generally, HGBDT models showed a relative advantage over RF models, as they required significantly less time for training while achieving comparable results. Therefore, they were efficient and performed better in terms of computational complexity.

Stability Improvement of Hybrid Renewable Energy Systems by Using Virtual Inertia Controller Based on Optimized FOPID with Harris Hawk Optimization

Stability Improvement of Hybrid Renewable Energy Systems by Using Virtual Inertia Controller Based on Optimized FOPID with Harris Hawk Optimization

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.

Conditional Generative Adversarial Networks with Optimized Machine Learning for Fault Detection of Triplex Pump in Industrial Digital Twin

In recent years, digital twin (DT) technology has garnered significant interest from both academia and industry. However, the development of effective fault detection and diagnosis models remains challenging due to the lack of comprehensive datasets. To address this issue, we propose the use of Generative Adversarial Networks (GANs) to generate synthetic data that replicate real-world data, capturing essential features indicative of health-related information without directly referencing actual industrial DT systems. This paper introduces an intelligent fault detection and diagnosis framework for industrial triplex pumps, enhancing fault recognition capabilities and offering a robust solution for real-time industrial applications within the DT paradigm. The proposed framework leverages Conditional GANs (CGANs) alongside the Harris Hawk Optimization (HHO) as a metaheuristic method to optimize feature selection from input data to enhance the performance of machine learning (ML) models such as Bagged Ensemble (BE), AdaBoost (AD), Support Vector Machine (SVM), K-Nearest Neighbors (KNNs), Decision Tree (DT), and Naive Bayes (NB). The efficacy of the approach is evaluated using key performance metrics such as accuracy, precision, recall, and F-measure on a triplex pump dataset. Experimental results indicate that hybrid-optimized ML algorithms (denoted by “ML-HHO”) generally outperform or match their classical counterparts across these metrics. BE-HHO achieves the highest accuracy at 95.24%, while other optimized models also demonstrate marginal improvements, highlighting the framework’s effectiveness for real-time fault detection in DT systems, where SVM-HHO attains 94.86% accuracy, marginally higher than SVM’s 94.48%. KNN-HHO outperforms KNNs with 94.73% accuracy compared to 93.14%. Both DT-HHO and DT achieve 94.73% accuracy, with DT-HHO exhibiting slightly better precision and recall. NB-HHO and NB show near-equivalent performance, with NB-HHO at 94.73% accuracy versus NB’s 94.6%. Overall, the optimized algorithms demonstrate consistent, albeit marginal, improvements over their classical versions.

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.

ViT-HHO: Optimized vision transformer for diabetic retinopathy detection using Harris Hawk optimization

Diabetic retinopathy (DR) is a significant cause of vision impairment globally, emphasizing the importance of timely and precise detection to prevent severe consequences. This study presents an optimized Vision Transformer (ViT) model that incorporates Harris Hawk Optimization (HHO) to improve the automated detection of diabetic retinopathy (DR). The ViT architecture utilizes self-attention mechanisms to capture local and global features in retinal images. Additionally, HHO optimizes key hyperparameters to maximize the performance of the model. The proposed ViT-HHO model achieved exceptional performance on the APTOS-2019 and IDRiD datasets. Specifically, it achieved 99.83 % accuracy, 99.78 % sensitivity, 99.85 % specificity, and 99.80 % AUC-ROC on the APTOS-2019 dataset, surpassing traditional CNNs and alternative optimization techniques. The model exhibited strong generalization on the IDRiID dataset, achieving an accuracy of 99.11 % and an AUC-ROC of 99.12 %. The ViT-HHO model demonstrates the potential for enhancing the clinical detection of diabetic retinopathy (DR), providing high precision and reliability.•An optimized Vision Transformer (ViT) model was developed using HHO for improved detection of Diabetic Retinopathy (DR).•The model was validated on the APTOS-2019 and IDRiID datasets, demonstrating superior accuracy and AUC-ROC metrics.•The model's generalization and robustness were demonstrated through comprehensive performance evaluations.

Landslide Displacement Prediction Using Kernel Extreme Learning Machine with Harris Hawk Optimization Based on Variational Mode Decomposition

Displacement deformation prediction is critical for landslide disaster monitoring, as a good landslide displacement prediction system helps reduce property losses and casualties. Landslides in the Three Gorges Reservoir Area (TGRA) are affected by precipitation and fluctuations in reservoir water level, and displacement deformation shows a step-like curve. Landslide displacement in TGRA is related to its geology and is affected by external factors. Hence, this study proposes a novel landslide displacement prediction model based on variational mode decomposition (VMD) and a Harris Hawk optimized kernel extreme learning machine (HHO-KELM). Specifically, VMD decomposes the measured displacement into trend, periodic, and random components. Then, the influencing factors are also decomposed into periodic and random components. The feature data, with periodic and random data, are input into the training set, and the trend, periodic, and random term components are predicted by HHO-KELM, respectively. Finally, the total predicted displacement is calculated by summing the predicted values of the three components. The accuracy and effectiveness of the prediction model are tested on the Shuizhuyuan landslide in the TGRA, with the results demonstrating that the new model provides satisfactory prediction accuracy without complex parameter settings. Therefore, under the premise of VMD effectively decomposing displacement data, combined with the global optimization ability of the HHO heuristic algorithm and the fast-learning ability of KELM, HHO-KELM can be used for displacement prediction of step-like landslides in the TGRA.

Employing an optimal control strategy for systems with measurement and actuation faults: a swarm optimization approach

ABSTRACT Various industries involve interconnected coupled tank systems to control fluid level, which poses significant challenge for traditional controllers. A tilt-integral-derivative controller offers enhanced adaptability for such systems. However, tuning of these controllers is highly challenging. This work focuses on enhancing the liquid level control in coupled tank systems using a tilt-integral-derivative controller. Tuning for the control algorithm is achieved through the application of Harris's hawks optimization. An objective function is designed by imposing constraints on performance metrics such as integral square error, integral absolute error, integral time absolute error, and closed-loop gain, which are then solved using Harris's hawks optimization. Further, the robust behaviour of the system is exhibited using μ-analysis to assess its stability. Simulation studies confirm the control algorithm's effectiveness, showing superior performance in reducing settling-time and minimizing overshoot. Additionally, the robustness of the controller in handling sensor and actuator faults under varying operating conditions is demonstrated.

Integration of machine learning and CFD for modeling mass transfer in water treatment using membrane separation process

This study aims to assess the efficacy of machine learning models in predicting solute concentration (C) distribution in a membrane separation process, using the input parameters which are spatial coordinates. Computational fluid dynamics (CFD) was employed with machine learning for simulation of process. The models evaluated include Kernel Ridge Regression (KRR), Radius nearest neighbor regression (RNN), K-nearest neighbors (KNN), LASSO, and Multi-Layer Perceptron (MLP). Additionally, Harris Hawks Optimization (HHO) was utilized to fine-tune the hyperparameters of these models. Leading the way is the MLP model, which achieves a remarkable test R2 value of 0.98637 together with very low RMSE and MAE values. Strongness and generalization capacity are shown by its consistent performance on test and training datasets. To conclude, the effectiveness of using machine learning regression methods more especially, KRR, KNN, RNN, LASSO, and MLP in estimating concentration from spatial coordinates was demonstrated in this work. For separation science via membranes where predictive modeling of spatial data is essential, the results offer important new perspectives by developing hybrid model.

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.

RNA-Seq analysis for breast cancer detection: a study on paired tissue samples using hybrid optimization and deep learning techniques

ProblemBreast cancer is a leading global health issue, contributing to high mortality rates among women. The challenge of early detection is exacerbated by the high dimensionality and complexity of gene expression data, which complicates the classification process.AimThis study aims to develop an advanced deep learning model that can accurately detect breast cancer using RNA-Seq gene expression data, while effectively addressing the challenges posed by the data’s high dimensionality and complexity.MethodsWe introduce a novel hybrid gene selection approach that combines the Harris Hawk Optimization (HHO) and Whale Optimization (WO) algorithms with deep learning to improve feature selection and classification accuracy. The model’s performance was compared to five conventional optimization algorithms integrated with deep learning: Genetic Algorithm (GA), Artificial Bee Colony (ABC), Cuckoo Search (CS), and Particle Swarm Optimization (PSO). RNA-Seq data was collected from 66 paired samples of normal and cancerous tissues from breast cancer patients at the Jawaharlal Nehru Cancer Hospital & Research Centre, Bhopal, India. Sequencing was performed by Biokart Genomics Lab, Bengaluru, India.ResultsThe proposed model achieved a mean classification accuracy of 99.0%, consistently outperforming the GA, ABC, CS, and PSO methods. The dataset comprised 55 female breast cancer patients, including both early and advanced stages, along with age-matched healthy controls.ConclusionOur findings demonstrate that the hybrid gene selection approach using HHO and WO, combined with deep learning, is a powerful and accurate tool for breast cancer detection. This approach shows promise for early detection and could facilitate personalized treatment strategies, ultimately improving patient outcomes.

Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites

This paper explores advanced machine learning approaches to enhance the prediction accuracy of compressive strength (CoS) in geopolymer composites (GePC). Geopolymers, as sustainable alternatives to Ordinary Portland Cement (OPC), offer significant environmental benefits by utilizing industrial by-products such as fly ash and ground granulated blast furnace slag (GGBS). The accurate prediction of their compressive strength is crucial for optimizing their mix design and reducing experimental efforts. We present a comparative analysis of two hybrid models, Harris Hawks Optimization with Random Forest (HHO-RF) and Sine Cosine Algorithm with Random Forest (SCA-RF), against traditional regression methods and classical models like the Extreme Learning Machine (ELM), General Regression Neural Network (GRNN), and Radial Basis Function (RBF). Using a comprehensive dataset derived from various scientific publications, we focus on key input variables including the fine aggregate, GGBS, fly ash, sodium hydroxide (NaOH) molarity, and others. Our results indicate that the SCA-RF model achieved a superior performance with a root mean square error (RMSE) of 1.562 and a coefficient of determination (R2) of 0.987, compared to the HHO-RF model, which obtained an RMSE of 1.742 and an R2 of 0.982. Both hybrid models significantly outperformed traditional methods, demonstrating their higher accuracy and reliability in predicting the compressive strength of GePC. This research underscores the potential of hybrid machine learning models in advancing sustainable construction materials through precise predictive modeling, paving the way for more environmentally friendly and efficient construction practices.

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.

A Novel Hybrid Harris Hawk Optimization–Sine Cosine Algorithm for Congestion Control in Power Transmission Network

In a deregulated power system, managing congestion is crucial for effective operation and control. The goal of congestion management is to alleviate transmission line congestion while adhering to system constraints at minimal cost. This research proposes a hybrid Harris Hawk Optimization–Sine Cosine Algorithm (hHHO-SCA) for an efficient generation rescheduling approach to achieve the lowest possible congestion cost. The hybridization has been performed by introducing the features of SCA in the HHO to boost the exploration and exploitation steps of HHO, providing an efficient global solution and effectively optimizing rescheduled power output. The effectiveness of this methodology is evaluated using IEEE 30 and IEEE 118-bus test systems, taking into account system parameters. The potency of the proposed method is analyzed by comparing the results of the hHHO-SCA with those from other recent optimization techniques. The findings show that the hHHO-SCA outperforms other methods by avoiding local optima and demonstrating promising convergence characteristics.

An efficient weighted slime mould algorithm for engineering optimization

In engineering applications, optimal parameter design is crucial. While Slime Mould Algorithm (SMA) excels in parameter discovery under constrained conditions, it faces challenges in achieving global convergence and avoiding local opsecttimal traps in complex tasks. This paper introduces an enhanced variant of SMA, termed CCHSMA, which integrates a Chaotic Local Search (CLS) mechanism to improve initial population diversity and combines Covariance Matrix Adaptation (CMA) and Harris Hawks Optimization (HHO) strategies to enhance global search efficiency. CCHSMA aims to improve search quality and reduce the likelihood of getting trapped in local optima. We evaluated CCHSMA's effectiveness by benchmarking it against the standard SMA and its variants using 30 CEC2017 test functions, and compared its performance with seven notable meta-heuristic algorithms and ten advanced swarm intelligence variants. The experimental results demonstrate that CCHSMA outperforms the other algorithms tested on the benchmark functions. To further validate its practical utility, CCHSMA's performance was also benchmarked against leading algorithms in real-world engineering applications. This paper uses detailed statistical methods, including the Wilcoxon signed-rank test and the Friedman test, to validate the comparative results. Our findings show that CCHSMA outperforms other algorithms in solving complex engineering optimization problems such as tension/compression spring design, pressure vessel design, and three-bar truss design, proving to be a robust tool for complex engineering optimization. Its enhanced initial population diversity and improved global search efficiency are essential for effectively addressing diverse engineering challenges.

Coordinated planning and operation of PV- hydrogen integrated distribution network incorporating daily-seasonal green hydrogen storage and EV charging station

The current study introduces an optimal planning and operational framework for a Distribution Network (DN) that integrates Photovoltaic (PV)-green Hydrogen (H2)-based energy system with Electric Vehicle Charging Station (EVCS). An efficient operational strategy is proposed considering both short-term H2 storage (STHS) and long-term H2 storage (LTHS), ensuring uninterrupted power supply. Besides, a novel Improved Harris Hawk Optimization method is developed to enhance the global search capabilities and convergence rate in optimizing the capacity of Solar module, Electrolyzer, and Fuel cells. The objective of the proposed model is to minimize the device costs, energy loss, and carbon emissions, adhering to all operational constraints. The proposed work is tested on 33-bus radial DN and 51-bus real Indian DN considering the time frame of 10 years. The analysis reveals that the PV-STHS-LTHS configuration, reduces total planning costs by 12% and 15% as compared to the base case in the 33-bus and 51-bus systems, respectively. Additionally, the integration of both daily and seasonal storage facilities with PV systems leads to a reduction in carbon emissions by 76% and 81% in the 33-bus and 51-bus systems, respectively. Besides the carbon reduction, the incorporation of daily and cross-seasonal H2 storage increases oxygen release into the environment by 13% for the 33-bus system and 15% for the 51-bus system.