Harris Hawks Optimization Research Articles

An Efficient Short-Term Solar Power Forecasting by Hybrid WOA-Based LSTM Model in Integrated Energy System

Objectives: Due to the irregular nature of sun irradiation and other meteorological conditions, solar power generation is constantly loaded with risks. When solar radiation data isn't captured and sky imaging equipment isn't available, improving forecasting becomes a more difficult endeavor. So our objective to improve the forecasting accuracy for next year solar power generation data. Methods: Our research used a real numerical solar power dataset of Australia and Germany and a standard approach for preprocessing. The feature selection in this research uses the Whale Optimization Algorithm (WOA). A Long Short-Term Memory (LSTM) method is utilized to determine the accuracy of solar power forecasts. The HHO (Harris Hawks Optimization) technique is also used to improve solar power forecasting accuracy. The performances were analyzed and the proposed method is employed in the python platform. Findings: The findings show that the suggested technique considerably increases the accuracy of short-term solar power forecasts for proposed method is 3.07 in comparison of LSTM and SVM at different data types and 15 min and 60 min interval. Novelty: The key novelties of this research is hybrid strategy for improving the precision of solar power forecasting for short periods of time. Including the Whale Optimization Algorithm (WOA), Long Short-Term Memory (LSTM), and Harris Hawks Optimization (HHO). Keywords: Power generation, Solar power forecasting, Whale optimization algorithm, Long Short­Term Memory, Harris hawk's optimization

Optimal placement of distribution network‐connected microgrids on multi‐objective energy management with uncertainty using the modified Harris Hawk optimization algorithm

AbstractConsidering the importance of the renewable energy sector in the distribution systems, energy operation, and management which are connected to the distribution network (DN) in the form of multiple microgrids (MMGs) is crucial in reducing cost and pollution. Hence, this paper aims to propose optimal energy management for MMGs in the DN. Different objective functions have been taken into account in this optimization, including network cost, pollution reduction, and distribution network power losses. To design the multi‐objective optimization problem, a fuzzy method has been adopted for simultaneous multi‐objective calculations. Furthermore, the effect of the placement of distributed generations (DGs) and microgrids (MGs) is considered to reduce the distribution network power losses. Information gap decision theory (IGDT) has formulated uncertainties about renewable sources and consumers. To solve this optimization problem, a new method of the modified Harris Hawk optimization (MHHO) algorithm has been implemented, compared with the original HHO and genetic algorithm (GA). Finally, the proposed method has been analysed under the IEEE 33‐bus distribution network for a 24‐hour time horizon, including three MGs considering different renewable energy sources (RESs). The simulation results have demonstrated the high performance of the allocated network with the MHHO algorithm compared to the other scenarios.

Improved Harris Hawks Optimizer algorithm to solve the multi-depot open vehicle routing problem

Improved Harris Hawks Optimizer algorithm to solve the multi-depot open vehicle routing problem

Retracted: An Effective Approach for Modular Community Detection in Bipartite Network Based on Integrating Rider with Harris Hawks Optimization Algorithms

Retracted: An Effective Approach for Modular Community Detection in Bipartite Network Based on Integrating Rider with Harris Hawks Optimization Algorithms

DNN-HHOA: Deep Neural Network Optimization-Based Tabular Data Extraction from Compound Document Images

Text information extraction from a tabular structure within a compound document image (CDI) is crucial to help better understand the document. The main objective of text extraction is to extract only helpful information since tabular data represents the relation between text lying in a tuple. Text from an image may be of low contrast, different style, size, alignment, orientation, and complex background. This work presents a three-step tabular text extraction process, including pre-processing, separation, and extraction. The pre-processing step uses the guide image filter to remove various kinds of noise from the image. Improved binomial thresholding (IBT) separates the text from the image. Then the tabular text is recognized and extracted from CDI using deep neural network (DNN). In this work, weights of DNN layers are optimized by the Harris Hawk optimization algorithm (HHOA). Obtained text and associated information can be used in many ways, including replicating the document in digital format, information retrieval, and text summarization. The proposed process is applied comprehensively to UNLV, TableBank, and ICDAR 2013 image datasets. The complete procedure is implemented in Python, and precision metrics performance is verified.

An efficient hybrid extreme learning machine and evolutionary framework with applications for medical diagnosis

AbstractIntegrating machine learning techniques into medical diagnostic systems holds great promise for enhancing disease identification and treatment. Among the various options for training such systems, the extreme learning machine (ELM) stands out due to its rapid learning capability and computational efficiency. However, the random selection of input weights and hidden neuron biases in the ELM can lead to suboptimal performance. To address this issue, our study introduces a novel approach called modified Harris hawks optimizer (MHHO) to optimize these parameters in ELM for medical classification tasks. By applying the MHHO‐based method to seven medical datasets, our experimental results demonstrate its superiority over seven other evolutionary‐based ELM trainer models. The findings strongly suggest that the MHHO approach can serve as a valuable tool for enhancing the performance of ELM in medical diagnosis.

Computer aided diagnosis using Harris Hawks optimizer with deep learning for pneumonia detection on chest X-ray images

Computer aided diagnosis using Harris Hawks optimizer with deep learning for pneumonia detection on chest X-ray images

Multi-strategy ensemble Harris hawks optimization for smooth path planning of mobile robots

Efficient and safe path planning for autonomous navigation is paramount in advancing the motion control capabilities of mobile robots. To obtain the global optimal smooth path for mobile robots, a multi-strategy ensemble Harris hawks optimization algorithm (SDHHO) is proposed in this paper. The spiral search strategy is adopted to improve the early update method of the algorithm, which can improve the global exploration ability. To achieve better balance between global exploration and local exploitation, the Sine chaotic map is introduced to the escape energy, replacing random components. Furthermore, an elite differential mutation strategy combined with Gaussian mutation is designed to prevent the algorithm from falling into local optima. We compared the SDHHO algorithm with other classical and novel algorithms on 23 benchmark functions, and the results demonstrated the superiority of SDHHO. The proposed algorithm is applied to the smooth path planning for mobile robots, which is transformed into an optimization problem of control nodes of high-order Bezier curves. Empirical evaluations across diverse environments underscore the proficiency of the proposed method in generating paths characterized by reduced length and enhanced safety and continuity.

MULTI-OBJECTIVE OPTIMIZATION AND PERFORMANCE ANALYSIS OF BIMORPH MAGNETO-ELECTRO-ELASTIC ENERGY HARVESTERS

This study thoroughly investigates the multi-objective optimization of a magneto-electro-elastic (MEE) harvester in bimorph configurations and by the new method of Harris Hawk’s optimization (HHO). The harvesters are configured in both series and parallel connections and under harmonic excitation to explore the effects of various parameters on the performance of the harvesting system. The primary objective is to maximize the total harvested power. Optimization involves various parameters, including dimensions, relative displacement changes, voltage, and current values. The Pareto fronts from the HHO method reveal optimal points in different configurations and scenarios. Notably, the optimal points are selected based on the criterion of maximum total power. The results reveal distinct optimal points for each objective function, demonstrating trade-offs between performance metrics. These findings provide valuable insights into the design and operation of efficient energy harvesters in MEE systems. The parallel configuration outperforms the series connection in terms of the current generation. Moreover, the evaluation of the overall performance of the energy harvesters in terms of total harvested power indicated that both series and parallel connections could lead to promising outcomes. However, the series connection exhibited a more dominant effect on maximizing the total harvested power, proving its relevance in pursuing the highest possible power output.

CMSV: a New Cloud Multi-Agents for Self-Driving Vehicles as a Services

The development of autonomous vehicles has changed the way of transmitting goods to users, with the potential to improve road safety and efficiency. One of the effective issues of self-driving vehicles is path planning issue. In this paper, a new system is proposed for addressing the potential of using multi-agent systems (MAS) to address path planning problems in autonomous vehicles. The system is called Cloud Multi-Agents for Self-Driving Vehicles as A Services (CMSV). It is used to solve the dependent delivery problem, where the vehicle transmits the goods/deliveries, that are dependent on each other, to specific locations in ordering or with specific directions. The new system consists of three phases: (1) converting the problem into TSP sub problems according to deliveries dependency, (2) applying GDC to select the good solutions (3) applying multi-agent system to select the optimal solution. The proposed system is compared with systems that use a genetic algorithm, Ant Colony algorithm, Particle Swarm Optimization and The Harris hawk optimization (HHO) algorithm. From the findings, we find that the proposed system is more efficient than the other systems.

Enhancing network stability in VANETs using nature inspired algorithm for intelligent transportation system.

The Internet of Vehicles (IoV) is one of the developing paradigms that integrates the automotive industry with the Internet of Things (IoT). The evolution of traditional Vehicular Ad-hoc Networks (VANETs), which are a layered framework for Intelligent Transportation Systems (ITS), is employed to provide Quality of Service (QoS) to end users in hazardous situations. VANETs can set up ad-hoc networks and share information among themselves using Peer-to-Peer (P2P) communication. Dynamic properties in VANETs such as dynamic topology, node mobility, sparse vehicle distribution, and bandwidth constraints can have an impact on scalability, routing, and security. This can result in frequent link failures, instability, reliability, and QOS concerns, as well as the inherent complexity of NP-hard problems. Researchers have proposed several techniques to achieve stability; the most prominent one is clustering, which relies on mobility metrics. However, existing clustering techniques generate overwhelming clusters, resulting in greater resource consumption, communication overhead, and hop count, which may lead to increased latency. Therefore, the primary objective is to achieve stability by increasing cluster lifetime, which is accomplished by generating optimal clusters. A nature-inspired meta-heuristic algorithm titled African Vulture Optimization Based Clustering Algorithm (AVOCA) is implemented to achieve it. The proposed algorithm can achieve load optimization with efficient resource utilization by mitigating hidden node challenges and ensuring communication proficiency. By maintaining an equilibrium state between the exploration and exploitation phases, AVOCA avoids local optima. The paper explores a taxonomy of the techniques used in Cluster Head (CH) selection, coordination, and maintenance to achieve stability with lower communication costs. We evaluated the effectiveness of AVOCA using various network grid sizes, transmission ranges, and network nodes. The results show that AVOCA generates 40% less clusters when compared to the Clustering Algorithm Based on Moth-Flame Optimization for VANETs (CAMONET). AVOCA generates 45% less clusters when compared to Self-Adaptive Multi-Kernel Clustering for Urban VANETs (SAMNET), AVOCA generates 43% less clusters when compared to Intelligent Whale Optimization Algorithm (i-WOA) and AVOCA generates 38% less clusters when compared to Harris Hawks Optimization (HHO). The results show that AVOCA outperforms state-of-the-art algorithms in generating optimal clusters.

A bi‐level planning strategy of a hydrogen‐supercapacitor hybrid energy storage system based on APA‐MOHHO

AbstractA bi‐level planning strategy of a hydrogen‐supercapacitor hybrid energy storage system (H‐S HESS) has been proposed in this study for wind power fluctuation suppression. The proposed system consists of a supercapacitor array and a hydrogen energy storage unit, and the bi‐level planning strategy consists of an Energy Management Level and a Capacity Configuration Level. In the upper level, with Hilbert–Huang transform frequency analysis, a novel energy management strategy based on adaptive power allocation (APA) is designed. The lower level applies multi‐objective Harris hawks optimizer to solve the optimal parameters of each device in the system. Based on the data measured from a 2‐MW wind system in a hydrogen‐electric coupled DC microgrid demonstration project, the optimal system capacity configuration scheme is obtained. The annualized cost of the system is $150,212, which is 41.1% lower than the scheme without optimization. The fluctuation unsuppressed rate of H‐S HESS is 0.64% under the APA strategy, which is only 0.14% higher than the conventional HESS (supercapacitor + lithium‐ion batteries) with the same annualized cost of system. The proposed strategy reduces the frequency of HESS operational power fluctuations, improves capacity configuration accuracy, and significantly decreases the cost gap between the proposed H‐S HESS and conventional structures.

Classification of anemia using Harris hawks optimization method and multivariate adaptive regression spline

Data mining methods are important for the diagnosis and prediction of diseases. Early and accurate diagnosis of patients is vital for their treatment. Various methods have been used in the literature to classify anemia. However, due to the different characteristics of patient datasets, changes in dataset sizes, different parameter numbers and features, and different numbers of patient records, algorithm performances vary according to datasets. In this study, the Harris hawks algorithm (HHA) and the multivariate adaptive regression spline (MARS) were used to classify anemia based on blood data of 1732 patients from the Kaggle database of patients with and without anemia. Six different algorithms were proposed to determine the parameters of the linear anemia approximation, namely multilinear form HHA, multilinear quadratic form HHA, multilinear exponential form HHA, first-order MARS model, second-order MARS model, and the best performing MARS model. The performance of the six proposed algorithms has been analyzed and found to be better than the previous studies in the literature.

Convergence of the Harris hawks optimization algorithm and fuzzy system for cloud-based task scheduling enhancement

Convergence of the Harris hawks optimization algorithm and fuzzy system for cloud-based task scheduling enhancement

CDMO: Chaotic Dwarf Mongoose Optimization Algorithm for feature selection

In this paper, a modified version of Dwarf Mongoose Optimization Algorithm (DMO) for feature selection is proposed. DMO is a novel technique of the swarm intelligence algorithms which mimic the foraging behavior of the Dwarf Mongoose. The developed method, named Chaotic DMO (CDMO), is considered a wrapper-based model which selects optimal features that give higher classification accuracy. To speed up the convergence and increase the effectiveness of DMO, ten chaotic maps were used to modify the key elements of Dwarf Mongoose movement during the optimization process. To evaluate the efficiency of the CDMO, ten different UCI datasets are used and compared against the original DMO and other well-known Meta-heuristic techniques, namely Ant Colony optimization (ACO), Whale optimization algorithm (WOA), Artificial rabbit optimization (ARO), Harris hawk optimization (HHO), Equilibrium optimizer (EO), Ring theory based harmony search (RTHS), Random switching serial gray-whale optimizer (RSGW), Salp swarm algorithm based on particle swarm optimization (SSAPSO), Binary genetic algorithm (BGA), Adaptive switching gray-whale optimizer (ASGW) and Particle Swarm optimization (PSO). The experimental results show that the CDMO gives higher performance than the other methods used in feature selection. High value of accuracy (91.9–100%), sensitivity (77.6–100%), precision (91.8–96.08%), specificity (91.6–100%) and F-Score (90–100%) for all ten UCI datasets are obtained. In addition, the proposed method is further assessed against CEC’2022 benchmarks functions.

A Statistical Prediction Model for Sluice Seepage Based on MHHO-BiLSTM

The current seepage prediction model of the sluice gate is rarely used. To solve the problem, this paper selects the bidirectional long and short-term neural network (BiLSTM) with high information integration and accuracy, which can well understand and capture the temporal pattern and dependency relationship in the sequence and uses the multi-strategy improved Harris Hawks optimization algorithm (MHHO) to analyze its two hyperparameters: By optimizing the number of forward and backward neurons, the overfitting and long-term dependence problems of the neural network are solved, and the convergence rate is accelerated. Based on this, the MHHO-BiLSTM statistical prediction model of sluice seepage is established in this paper. To begin with, the prediction model uses water pressure, rainfall, and aging effects as input data. Afterward, the bidirectional long short-term memory neural network parameters are optimized using the multi-strategy improved Harris Hawks optimization algorithm. Then, the statistical prediction model based on the optimization algorithm proposed in this paper for sluice seepage is proposed. Finally, the seepage data of a sluice and its influencing factors are used for empirical analysis. The calculation and analysis results indicate that the optimization algorithm proposed in this paper can better search the optimal parameters of the bidirectional long short-term memory neural network compared with the original Harris Eagle optimization algorithm, optimizing the bidirectional long short-term memory neural network (HHO-BiLSTM) and the original bidirectional long short-term memory neural network (BiLSTM). Meanwhile, the bidirectional long and short-term neural network (BiLSTM) model shows higher prediction accuracy and robustness.

ONBLR: An effective optimized ensemble ML approach for classifying liver cirrhosis disease

Liver cirrhosis is a late stage of scarring (fibrosis) of the liver caused by many forms of liver diseases and conditions, such as hepatitis and chronic alcoholism. Early diagnosis and management can help slow the progression of the disease and improve quality of life. Developing more accurate and non-invasive methods for the early detection of liver cirrhosis to enhance timely intervention and improve patient outcomes. Effective prediction reduces overall cirrhosis patients' mortality due to various bleeding, making further study in this field a significant task. Therefore, we propose a novel ensemble machine learning based approach for classification. Label encoding and data normalization using the min-max approach are performed in preprocessing. Then, characteristics such as age, gender, Liver function tests (AST, ALT, alkaline phosphatase, bilirubin) and Medical history and comorbidities are retrieved using the ConvNeXt approach. Using the Improved Grasshopper optimization algorithm (IGOA), the essential features that increase the accuracy even more were selected. Finally, the optimized ensemble machine learning approach naïve Bayes and logistic regression (ONBLR) is employed to classify the liver cirrhosis disease. Harris Hawks optimization algorithm is employed to optimize the hyperparameters. The proposed approach is compared with the existing state-of-the-art machine learning approaches. While compared with existing approaches, the proposed model yields 99.18 % accuracy, 99.12 % of Sensitivity and 98.92 % of specificity which is greater among other approaches.

Leveraging Neutrosophic TOPSIS with Artificial Intelligence-Driven Tropical Cyclone Intensity Estimation for Weather Prediction

Tropical cyclones (TCs) are powerful, low-pressure weather systems attributed to heavy rainfall and strong winds, and have often resulted in extensive damage to coastal regions. TC intensity prediction, an essential aspect of meteorological forecasting, includes evaluating the strength of the storm to facilitate disaster preparedness and alleviate possible risks. Classical approaches for the prediction of TC intensity rely on different oceanic and atmospheric parameters, but the incorporation of artificial intelligence (AI) approaches, especially those leveraging image data, provides positive breakthroughs in efficiency and accuracy. By harnessing AI techniques like deep learning architectures and convolutional neural networks (CNNs), meteorologists could analyze radar data, satellite imagery, and other visual inputs to distinguish complicated patterns indicative of intensity changes and TC development. This combination of weather science and AI-driven image analysis enables more timely and precise predictions and improves our understanding of TC dynamics, eventually fortifying protection against the impacts of formidable storms. This article introduces Neutrosophic TOPSIS with Artificial Intelligence Driven Tropical Cyclone Intensity Estimation (NTOPSIS-TCIE) technique for Weather Prediction. The presented NTOPSIS-TCIE technique determines the intensities of the TC which in turn helps to forecast weather. In the NTOPSIS-TCIE technique, median filtering (MF) approach is used to remove the noise in the images. In addition, the features are extracted using deep convolutional neural network (CNN) model. To enhance the performance of the CNN model, Harris Hawks Optimization (HHO) algorithm is applied. Finally, the NTOPSIS model is employed for the prediction of TC intensities. The performance of the NTOPSIS-TCIE technique can be studied using TC image dataset and the results signify its promising results over other models

Efficient Sink Node Position Estimation using Harris Hawks Optimization Algorithm in Wireless Sensor Networks

Wireless sensor network (WSN) was utilized widely in numerous areas owing to their accessibility in data collection, processing, and transmission, and the strength and reliability of data processing and transmission are based on the accuracy of the positions of sensor nodes (SNs) in the WSN. Sink node location estimation in WSN is a vital task intended to define the geographical position of the sink node in the network area of coverage. This procedure normally includes using numerous localization techniques that trust data like received signal strength, arrival time, time variance of arrival, or angle of arrival from adjacent SNs. The accuracy of sink node localization directly influences the efficiency of data aggregation, routing procedures, and complete performance of the network in tasks like environmental monitoring, target tracking, and event recognition. As WSNs are frequently used in remote environments where physical involvement is unusable, an effective and accurate sink node localization model plays a vital part in certifying the network's longevity and reliability. This study develops an Efficient Sink Node Position Estimation using the Harris Hawks Optimization (SNPE-HHO) Algorithm in WSN. The main intention of the SNPE-HHO technique is to recognize the optimal position of the sink node in the network. To achieve this, the SNPE-HHO technique employs the HHO system which gets inspiration from the hunting tactics of Harris Hawk. Moreover, the SNPE-HHO technique computes a fitness function that can drive the searching direction of the HHO algorithm and enhance the node estimation performance. The performance analysis of the SNPE-HHO method is verified by utilizing distinct metrics. The experimentation values confirmed the improved estimation performance of the SNPE-HHO technique over other existing methods

Enhanced Randomized Harris Hawk Optimization of PI controller for power flow control in the microgrid with the PV-wind-battery system

Microgrids, characterized by their ability to work individually or in combination with the main power system, play a pivotal role in addressing the growing demand for reliable and sustainable energy solutions. This work concentrates on the integration of sustainable energy sources, specifically photovoltaic (PV), and wind generation and a battery storage system within a microgrid framework. Additionally, a power flow control strategy is implemented to enhance the dynamic behaviour and stability of the microgrid. The proportional-integral (PI) controller is a fundamental component in regulating the microgrid’s power flow, ensuring optimal performance under varying operating conditions. However, tuning the PI controller parameters is a difficult task because of the dynamic and nonlinear nature of renewable energy sources. In this work, the application of the Enhanced Randomized Harris Hawk Optimization (ERHHO) to fine-tune the PI controller is proposed, using the algorithm’s ability to mimic the hunting behaviour of hawks in finding optimal solutions. The PV-Wind-Battery microgrid system is modelled, and the proposed algorithm is employed to optimize the PI controller parameters for efficient energy management. The ERHHO algorithm’s exploration-exploitation balance is harnessed to navigate the complex solution space and converge to optimal PI controller settings, thereby enhancing the microgrid’s stability and performance. The study evaluates the effectiveness of the proposed ERHHO-based PI controller tuning through comprehensive simulations. Performance metrics such as transient response, overshoot, settling time, and steady-state error are analysed to validate the robustness and efficiency of the proposed method. Compared to its nearest optimization algorithm, with the proposed algorithm rise time is reduced by 50%, overshoot is reduced by 75%, settling time is reduced by 66%, and finally, a percentage of reduction of steady-state error is 45%. The outcomes of this research contribute to the advancement of microgrid control strategies, offering a novel approach to PI controller tuning in the context of diverse renewable energy sources. The integration of the Harris Hawk Optimization algorithm provides a promising avenue for enhancing the operational efficiency and reliability of microgrids, paving the way for sustainable and resilient energy systems in the aspect of growing energy landscapes.