Harris Hawks Research Articles

Dynamic Harris hawks optimizer based on historical information and tournament strategy and its application in numerical optimization of blast furnace ingredients

Harris hawks optimizer (HHO) is a new meta-heuristic optimization algorithm, which is inspired by the cooperative behavior and chasing style of Harris hawks in nature. However, HHO can neither balance exploration and exploitation well nor fully use historical information in the face of complex optimization problems. In order to alleviate the shortcomings of HHO, a dynamic Harris hawks optimizer based on historical information and tournament strategy (DHHO-HITS) is proposed in this paper, in which a dynamic parameter is proposed to adjust the behavior of the Harris hawks in exploration and exploitation. The concept of “archive” is added to store the historical optimal solutions, and then a randomly selected historical optimal solution in the “archive” is used to guide the Harris hawk population, which improves the utilization rate of historical information and the accuracy of the solution. The tournament strategy is used to select a new generation of the population to avoid premature convergence. To verify the performance of DHHO-HITS, the CEC2020 benchmark suite is used to analyze the selection of parameters, the influence of the control parameters, the influence of three improved mechanisms, and the dynamic properties of the algorithm. Then, DHHO-HITS is compared with 20 other algorithms on multiple dimensions of the CEC2017 benchmark suite, CEC2020 benchmark suite, and CEC2021 benchmark suite. Further, the proposed DHHO-HITS is applied to three standard engineering problems. These test results show that DHHO-HITS outperforms most competitors in numerical optimization. Finally, DHHO-HITS is used to solve the numerical optimization of blast furnace ingredients. The simulation results based on actual data show that the optimized ingredient scheme can reduce the CO2 emissions of the blast furnace while meeting multiple constraints: for each ton of iron output, the CO2 emissions are reduced by 85.7177 kg, accounting for about 9.97% of the total emissions.

Enhancing the multiple traveling salesman problem solutions through Harris Hawks optimization and machine learning techniques

Enhancing the multiple traveling salesman problem solutions through Harris Hawks optimization and machine learning techniques

A rhinopithecus swarm optimization algorithm for complex optimization problem

This paper introduces a novel meta-heuristic algorithm named Rhinopithecus Swarm Optimization (RSO) to address optimization problems, particularly those involving high dimensions. The proposed algorithm is inspired by the social behaviors of different groups within the rhinopithecus swarm. RSO categorizes the swarm into mature, adolescent, and infancy individuals. Due to this division of labor, each category of individuals employs unique search methods, including vertical migration, concerted search, and mimicry. To evaluate the effectiveness of RSO, we conducted experiments using the CEC2017 test set and three constrained engineering problems. Each function in the test set was independently executed 36 times. Additionally, we used the Wilcoxon signed-rank test and the Friedman test to analyze the performance of RSO compared to eight well-known optimization algorithms: Dung Beetle Optimizer (DBO), Beluga Whale Optimization (BWO), Salp Swarm Algorithm (SSA), African Vultures Optimization Algorithm (AVOA), Whale Optimization Algorithm (WOA), Atomic Retrospective Learning Bare Bone Particle Swarm Optimization (ARBBPSO), Artificial Gorilla Troops Optimizer (GTO), and Harris Hawks Optimization (HHO). The results indicate that RSO exhibited outstanding performance on the CEC2017 test set for both 30 and 100 dimension. Moreover, RSO ranked first in both dimensions, surpassing the mean rank of the second-ranked algorithms by 7.69% and 42.85%, respectively. Across the three classical engineering design problems, RSO consistently achieves the best results. Overall, it can be concluded that RSO is particularly effective for solving high-dimensional optimization problems.

A Harris Hawks optimization-based cellular automata model for urban growth simulation

A Harris Hawks optimization-based cellular automata model for urban growth simulation

Power quality enhancement in the wind energy distribution system using HHO algorithm based UPFC

ABSTRACT This study proposes using a Wind Energy Conversion System (WECS) to power a Unified Power Flow Controller (UPFC) Direct Current (DC) capacitor, maintaining appropriate voltage levels. This arrangement allows UPFC adjustment to improve Power Quality (PQ). The main issue rapid growth of nonlinear loads causes sudden variations in source voltage, resulting in a reduction in power quality within seconds. UPFC is used to address these concerns. This work uses the Harris Hawks Optimizer (HHO) to improve the performance of the UPFC. To begin, the aim function parameters are defined, including voltage, current, active power, and reactive power. Thus, these characteristics are used to generate control pulses for the series and shunt active power filters. HHO is used to find the best solution and predict the ideal UPFC control signal. Additionally, voltage instability and power dissipation decrease under different load conditions. Thus, system power supply quality improves. To evaluate the effectiveness of the proposed technique, two scenarios were identified: fault circumstances and engine speed changes. MATLAB/Simulink was used to implement the suggested strategy and compare it to established methodologies like Grey Wolf Optimization (GWO), Modified Particle Swarm Optimization (MPSO), and Whale Optimization Algorithm (WOA).

Multiobjective Harris Hawks Optimization-Based Task Scheduling in Cloud-Fog Computing

Multiobjective Harris Hawks Optimization-Based Task Scheduling in Cloud-Fog Computing

An enhanced stability evaluation system for entry-type excavations: Utilizing a hybrid bagging-SVM model, GP and kriging techniques

In underground mining, especially in entry-type excavations, the instability of surrounding rock structures can lead to incalculable losses. As a crucial tool for stability analysis in entry-type excavations, the critical span graph must be updated to meet more stringent engineering requirements. Given this, this study introduces the support vector machine (SVM), along with multiple ensemble (bagging, adaptive boosting, and stacking) and optimization (Harris hawks optimization (HHO), cuckoo search (CS)) techniques, to overcome the limitations of the traditional methods. The analysis indicates that the hybrid model combining SVM, bagging, and CS strategies has a good prediction performance, and its test accuracy reaches 0.86. Furthermore, the partition scheme of the critical span graph is adjusted based on the CS-BSVM model and 399 cases. Compared with previous empirical or semi-empirical methods, the new model overcomes the interference of subjective factors and possesses higher interpretability. Since relying solely on one technology cannot ensure prediction credibility, this study further introduces genetic programming (GP) and kriging interpolation techniques. The explicit expressions derived through GP can offer the stability probability value, and the kriging technique can provide interpolated definitions for two new subclasses. Finally, a prediction platform is developed based on the above three approaches, which can rapidly provide engineering feedback.

Optimal sizing of a fixed-tilt ground-mounted grid-connected photovoltaic system with bifacial modules using Harris Hawks Optimization

This paper presents an optimal design for ground-mounted grid-connected bifacial PV power plants using a Computational Intelligence (CI)- based Harris Hawks Optimization (HHO) algorithm. This HHO algorithm identifies the best configuration of components and installation parameters for the bifacial PV power plant, aiming to maximize the final yield, minimize the Levelized Cost of Electricity, and boost the Net Present Value. Four variables were optimized: the bifacial PV module model, inverter model, tilt angle, and module elevation. Furthermore, the paper introduces a Harris Hawks Optimization Sizing Algorithm (HHOSA) to address the sizing challenges. The presented HHOSA was purely developed in Matlab R2017b. The usage of PVsyst was only limited to the derivation of irradiation data at different tilt angle of PV array. These data were later used in HHOSA. To verify its effectiveness, HHOSA was benchmarked against other CI algorithms, including the Slime Mould Algorithm (SMA), Firefly Algorithm (FA), Manta Ray Foraging Optimization (MRFO), and Cuckoo Search Algorithm (COA). The evaluation considered the algorithm’s stability, local search capability, convergence rate, computation time, and required population size. Findings suggest that the HHOSA outperforms its peers, marking it as a potential leader for designing bifacial PV power plants. The results indicate that the HHOSA algorithm exhibits superior performance in these aspects, making it a promising approach for optimizing the design of bifacial PV power plants. Moreover, this study provides insights into the economic and technical viability of bifacial PV systems under various environmental and system conditions. A sensitivity analysis, focusing on the interplay of three decision variables − albedo values (25 %, 50 %, and 75 %), tilt angles (10°, 25°, and 35°), and module elevations (0.5 m, 1.5 m, and 2 m) − was conducted. It assessed their influence on final yield, additional bifacial PV module yield, Levelized Cost of Electricity, and the system’s Net Present Value. The results emphasize the importance of carefully considering the impacts of albedo, module elevation, and tilt angle on the financial performance of bifacial PV installations.

A Novel Feature Selection Strategy Based on the Harris Hawks Optimization Algorithm for the Diagnosis of Cervical Cancer

Cervical cancer is the fourth most commonly diagnosed cancer and one of the leading causes of cancer-related deaths among females worldwide. Early diagnosis can greatly increase the cure rate for cervical cancer. However, due to the need for substantial medical resources, it is difficult to implement in some areas. With the development of machine learning, utilizing machine learning to automatically diagnose cervical cancer has currently become one of the main research directions in the field. Such an approach typically involves a large number of features. However, a portion of these features is redundant or irrelevant. The task of eliminating redundant or irrelevant features from the entire feature set is known as feature selection (FS). Feature selection methods can roughly be divided into three types, including filter-based methods, wrapper-based methods, and embedded-based methods. Among them, wrapper-based methods are currently the most commonly used approach, and many researchers have demonstrated that these methods can reduce the number of features while improving the accuracy of diagnosis. However, this method still has some issues. Wrapper-based methods typically use heuristic algorithms for FS, which can result in significant computational time. On the other hand, heuristic algorithms are often sensitive to parameters, leading to instability in performance. To overcome this challenge, a novel wrapper-based method named the Binary Harris Hawks Optimization (BHHO) algorithm is proposed in this paper. Compared to other wrapper-based methods, the BHHO has fewer hyper-parameters, which contributes to better stability. Furthermore, we have introduced a rank-based selection mechanism into the algorithm, which endows BHHO with enhanced optimization capabilities and greater generalizability. To comprehensively evaluate the performance of the proposed BHHO, we conducted a series of experiments. The experimental results show that the proposed BHHO demonstrates better accuracy and stability compared to other common wrapper-based FS methods on the cervical cancer dataset. Additionally, even on other disease datasets, the proposed algorithm still provides competitive results, proving its generalizability.

Credit card fraud detection using the brown bear optimization algorithm

Fraud detection in banking systems is crucial for financial stability, customer protection, reputation management, and regulatory compliance. Machine Learning (ML) is vital in improving data analysis, real-time fraud detection, and developing fraud techniques by learning from data and adjusting detection strategies accordingly. Feature Selection (FS) is essential for enhancing fraud detection through ML to achieve optimal model accuracy. This is because it helps to eliminate the negative impact of redundant and irrelevant attributes. To enhance the accuracy of the given dataset, the researchers utilized multiple methods to determine the most fitting features. However, it is important to note that when implementing these methods on datasets with larger feature sizes, they may encounter issues with local optimality. Despite this, the researchers continue to work on improving the effectiveness of these methods. This study presents an effective methodology based on the Brown-Bear Optimization (BBO) algorithm to enhance the capacity to accurately identify financial CCF transactions by recognizing pertinent features. BBO has balanced capabilities to reduce dimensionality while enhancing classification accuracy. It is improved by adjusting the positions randomly to enhance exploration and exploitation capabilities, and then it is cloned into a binary variant named Binary BBOA (BBBOA). The Support Vector Machine (SVM), k-nearest Neighbor (k-NN), and Xgb-tree are the ML classifiers used with the suggested methodology. On the Australian credit dataset, the proposed methodology is compared with the basic BBOA and ten current optimizers, such as Binary African Vultures Optimization (BAVO), Binary Salp Swarm Algorithm (BSSA), Binary Atom Search Optimization (BASO), Binary Henry Gas Solubility Optimization (BHGSO), Binary Harris Hawks Optimization (BHHO), Binary Bat Algorithm (BBA), Binary Particle Swarm Optimization (BPSO), Binary Grasshopper Optimization Algorithm (BGOA), and Binary Sailfish Optimizer (BSFO). Regarding Wilcoxon’s rank-sum test (α=0.05), the superiority and effective consequence of the presented methodology are clear on the utilized dataset and got an accuracy of classification up to 91% in the utilized dataset combined with an attribute reduction length down to 67%. The proposed methodology is further validated using 10 benchmark datasets and outperformed its competitors in most utilized datasets regarding different performance measures. In the end, the proposed methodology is further validated using ten benchmark datasets from the UCI repository. It outperformed its competitors in most of the utilized datasets regarding different performance measures.

Energy-efficient cluster head selection in wireless sensor networks-based internet of things (IoT) using fuzzy-based Harris hawks optimization

Energy-efficient cluster head selection in wireless sensor networks-based internet of things (IoT) using fuzzy-based Harris hawks optimization

A Novel Hybrid Algorithm Based on Beluga Whale Optimization and Harris Hawks Optimization for Optimizing Multi-Reservoir Operation

A Novel Hybrid Algorithm Based on Beluga Whale Optimization and Harris Hawks Optimization for Optimizing Multi-Reservoir Operation

Enhanced Target Localization in the Internet of Underwater Things through Quantum-Behaved Metaheuristic Optimization with Multi-Strategy Integration

Underwater localization is considered a critical technique in the Internet of Underwater Things (IoUTs). However, acquiring accurate location information is challenging due to the heterogeneous underwater environment and the hostile propagation of acoustic signals, especially when using received signal strength (RSS)-based techniques. Additionally, most current solutions rely on strict mathematical expressions, which limits their effectiveness in certain scenarios. To address these challenges, this study develops a quantum-behaved meta-heuristic algorithm, called quantum enhanced Harris hawks optimization (QEHHO), to solve the localization problem without requiring strict mathematical assumptions. The algorithm builds on the original Harris hawks optimization (HHO) by integrating four strategies into various phases to avoid local minima. The initiation phase incorporates good point set theory and quantum computing to enhance the population quality, while a random nonlinear technique is introduced in the transition phase to expand the exploration region in the early stages. A correction mechanism and exploration enhancement combining the slime mold algorithm (SMA) and quasi-oppositional learning (QOL) are further developed to find an optimal solution. Furthermore, the RSS-based Cramér–Raolower bound (CRLB) is derived to evaluate the effectiveness of QEHHO. Simulation results demonstrate the superior performance of QEHHO under various conditions compared to other state-of-the-art closed-form-expression- and meta-heuristic-based solutions.

A Novel Techno-Economical Control of UPFC against Cyber-Physical Attacks Considering Power System Interarea Oscillations

In the field of electrical engineering, there is an increasing concern among managers and operators about the secure and cost-efficient operation of smart power systems in response to disturbances caused by physical cyber attacks and natural disasters. This paper introduces an innovative framework for the hybrid, coordinated control of Unified Power Flow Controllers (UPFCs) and Power System Stabilizers (PSSs) within a power system. The primary objective of this framework is to enhance the system’s security metrics, including stability and resilience, while also considering the operational costs associated with defending against cyber-physical attacks. The main novelty of this paper lies in the introduction of a real-time online framework that optimally coordinates a power system stabilizer, power oscillation damper, and unified power flow controller to enhance the power system’s resilience against transient disturbances caused by cyber-physical attacks. The proposed approach considers technical performance indicators of power systems, such as voltage fluctuations and losses, in addition to economic objectives, when determining the optimal dynamic coordination of UPFCs and PSSs—aspects that have been neglected in previous modern research. To address the optimization problem, a novel multi-objective search algorithm inspired by Harris hawks, known as the Multi-Objective Harris Hawks (MOHH) algorithm, was developed. This algorithm is crucial in identifying the optimal controller coefficient settings. The proposed methodology was tested using standard IEEE9-bus and IEEE39-bus test systems. Simulation results demonstrate the effectiveness and efficiency of this approach in achieving optimal system recovery, both technically and economically, in the face of cyber-physical attacks.

A gazelle optimization expedition for key term separated fractional nonlinear systems with application to electrically stimulated muscle modeling

Various real-time processes are effectively represented with a fractional nonlinear autoregressive exogenous (F-NARX) model where estimation of model parameters is considered as an essential task. In this study, a population-based gazelle optimization algorithm (GOA) inspired by the evolutionary characteristics of gazelle's is exploited for the parameter estimation of the key term-separated F-NARX system. The Grünwald-Letnikov derivative, a fractional order calculus operator is integrated to develop the F-NARX system from a conventional non-linear autoregressive system. The mean square error-based merit function is developed and the effectiveness of the GOA for the F-NARX system is analyzed in terms of speedy convergence, estimation accuracy, complexity and robustness for different noise scenarios. The extendibility of the GOA is assessed through the estimation of stiff parameters of the electrically stimulated muscle model (ESMM) required for rehabilitation of paralyzed muscles. The efficacy of the GOA is endorsed through Wilcoxon signed rank statistical test in comparison with recent counterparts of Runge Kutta optimization algorithm, Whale optimization algorithm, and Harris Hawks optimization algorithm.

Gene selection based on recursive spider wasp optimizer guided by marine predators algorithm

Detecting tumors using gene analysis in microarray data is a critical area of research in artificial intelligence and bioinformatics. However, due to the large number of genes compared to observations, feature selection is a central process in microarray analysis. While various gene selection methods have been developed to select the most relevant genes, these methods’ efficiency and reliability can be improved. This paper proposes a new two-phase gene selection method that combines the ReliefF filter method with a novel version of the spider wasp optimizer (SWO) called RSWO-MPA. In the first phase, the ReliefF filter method is utilized to reduce the number of genes to a reasonable number. In the second phase, RSWO-MPA applies a recursive spider wasp optimizer guided by the marine predators algorithm (MPA) to select the most informative genes from the previously selected ones. The MPA is used in the initialization step of recursive SWO to narrow down the search space to the most relevant and accurate genes. The proposed RSWO-MPA has been implemented and validated through extensive experimentation using eight microarray gene expression datasets. The enhanced RSWO-MPA is compared with seven widely used and recently developed meta-heuristic algorithms, including Kepler optimization algorithm (KOA), marine predators algorithm (MPA), social ski-driver optimization (SSD), whale optimization algorithm (WOA), Harris hawks optimization (HHO), artificial bee colony (ABC) algorithm, and original SWO. The experimental results demonstrate that the developed method yields the highest accuracy, selects fewer features, and exhibits more stability than other compared algorithms and cutting-edge methods for all the datasets used. Specifically, it achieved an accuracy of 100.00%, 94.51%, 98.13%, 95.63%, 100.00%, 100.00%, 92.97%, and 100.00% for Yeoh, West, Chiaretti, Burcyznski, leukemia, ovarian cancer, central nervous system, and SRBCT datasets, respectively.

Hybridized machine learning models for phosphate pollution modeling in water systems for multiple uses

Phosphate pollution in water bodies is a significant environmental concern, especially in regions with extensive agricultural practices. Hence, a tool for accurately assessing the phosphate concentration is essential. This research paper explores the effectiveness of machine learning (ML) models combined with nature-inspired optimization algorithms for predicting phosphate levels in water systems. The novelty consists of integrating the power of machine learning models, which have been presenting excellent performance at capturing complex relationships in environmental pollution data, with the Harris Hawks Optimizer (HHO) optimization capabilities inspired by hawks' hunting behavior. Four hybrid implementations combining the HHO were evaluated, and four feature subsets were assessed to identify the most influential variables in the modeling process. Using water quality data from Brazilian upstream watersheds, the hybrid models were trained and validated, enabling accurate and robust predictions of phosphate concentrations. The elastic net (EN) model optimized by Harris Hawk Optimizer (HHO-EN) produced the best-averaged performance among all experiments (R = 0.825, R2 = 0.670, Root Mean Square Error (RMSE) = 0.049 mg/L, Mean Absolute Error (MAE) = 0.037 mg/L). A parametric and feature importance analysis identified the most influential parameters in the contamination modeling process. Hybrid machine learning models represent a novel and efficient strategy for water quality monitoring and environmental management, supporting the preservation of aquatic ecosystems.

Settlement Prediction for Concrete Face Rockfill Dams Considering Major Factor Mining Based on the HHO-VMD-LSTM-SVR Model

Some important discoveries have been revealed in some studies, including that the settlement of concrete face rockfill dams (CFRDs) may cause cracks in the concrete face slabs, which may lead to dam collapse. Therefore, deformation behavior prediction of CFRDs is a longstanding and emerging aspect of dam safety monitoring. This paper aims to propose a settlement prediction model for CFRDs combining the variational mode decomposition (VMD) algorithm, long short-term memory (LSTM) network, and support vector regression algorithm (SVR). Firstly, VMD is applied in the decomposition of dam settlement monitoring data to reduce its complexity. Furthermore, feature information on settlement time series is extracted. Secondly, the LSTM and SVR are optimized by the Harris hawks optimization (HHO) algorithm and modified least square (PLS) method to mine the major influencing factors and establish the prediction model with higher precision. Finally, the proposed model and other models are applied to predict the deformation behavior of the Yixing CFRD. Prediction results indicate that the proposed method possesses particular advantages over other models. The proposed VMD-LSTM-SVR model might help to evaluate the settlement trends and safety states of CFRDs.

Adaptive Channel Equalization for Digital Communication with Tunicate Swarm Algorithm

In wireless communication systems, the information is transferred as digital symbols these symbols are affected by noise interference while transmitting through the wireless channel. In today’s world, the increased demand for rapid data transmission rates leads to more inter-symbol interference in the received signal. To diminish the effects of inter-symbol interference adaptive channel equalization is utilized in digital communication. In this paper, the inter-symbol interference effect in the finite impulse response channel is reduced in terms of an adaptive equalizer method. The weights/coefficients of the equalizer are optimized through a tunicate swarm algorithm. Channel equalization involves optimizing the channel coefficients to mitigate the impact of inter-symbol interference. This equalization process can be viewed as an iterative optimization task, aimed at lessening the mean square error among the transmitted signal and the equalizer’s output. The implementation of the proposed method is executed using MATLAB software. We assess its effectiveness through a comparative analysis, considering metrics such as mean square error, learning rate, bit error rate, convergence rate, and computational time. Furthermore, we conduct a comparative evaluation with other optimization approaches, including the Bat Algorithm, Slime Mould Algorithm, and Harris Hawks Optimization Algorithm. This comparison demonstrates the superiority of the proposed algorithm over traditional optimization techniques.

An improved Harris Hawks optimizer based feature selection technique with effective two-staged classifier for network intrusion detection system

An improved Harris Hawks optimizer based feature selection technique with effective two-staged classifier for network intrusion detection system