Harris Hawks Optimization Algorithm Research Articles

An Improved Harris Hawks Optimization Algorithm Based on Bi-Goal Evolution and Multi-Leader Selection Strategy for Multi-Objective Optimization

An Improved Harris Hawks Optimization Algorithm Based on Bi-Goal Evolution and Multi-Leader Selection Strategy for Multi-Objective Optimization

A prospective approach to detect advanced persistent threats: Utilizing hybrid optimization technique

Advanced Persistent Threat (APT) attacks pose significant challenges for AI models in detecting and mitigating sophisticated and highly effective cyber threats. This research introduces a novel concept called Hybrid HHOSSA which is the grouping of Harris Hawk Optimization (HHO) and Sparrow Search Algorithm (SSA) characteristics for optimizing the feature selection and data balancing in the context of APT detection. In addition, the light GBM as well as the weighted average Bi-LSTM are optimized by the proposed hybrid HHOSSA optimization. The HHOSSA-based attribute selection is used to choose the most important attributes from the provided dataset in the early step of the quasi-identifier detection. The HHOSSA-SMOTE algorithm effectively balances the unbalanced data, such as the lateral movements and the data exfiltration in the DAPT 2020 database, which further improves the classifier performance. The light GBM and the Bi-LSTM classifier hyperparameters are well attuned and classified by the HHOSSA optimization for the precise classification of the attacks. The outcome of both the optimized light GBM and the Bi-LSTM classifier generates the final prediction of the attacks existing in the network. According to the research findings, the HHOSSA-hybrid classifier achieves high accuracy in detecting attacks, with an accuracy rate of 94.468 %, a sensitivity of 94.650 %, and a specificity of 95.230 % with a K-fold value of 10. Also, the HHOSSA-hybrid classifier achieves the highest AUC percentage of 97.032, highlighting its exceptional performance in detecting APT attacks.

A robot path planning method using improved Harris Hawks optimization algorithm

The traditional Harris Hawks optimization algorithm is prone to the local shortest path, slow search speed and poor path accuracy in indoor mobile robot path planning. For the above problems, a multi-strategy improvement of the Harris Hawks optimization algorithm (MIHHO) is proposed. In this study, a Chebyshev chaotic mapping strategy is used to increase the diversity of the Harris Hawk population, improve the global search performance of the Harris Hawk algorithm, and prevent being trapped in the locally optimal path. A fusion exploration mechanism is proposed to fuse the discovery mechanism of the sparrow algorithm with the exploration mechanism of the HHO. Then the influence factor E is improved to improve the algorithm’s search accuracy and search efficiency, and finally, in the design of a dynamic Lévy flight strategy, which accelerates the convergence speed of the algorithm and improves the robot planning speed. Simulation results show that the proposed MIHHO method exhibits better search performance in path planning, improved planning efficiency, and superior quality of planned paths.

Prediction of thermal comfort indoors and cooling loads based on reasonable zoning using the improved HHO with multi-strategy fusion-FENN algorithm

A comfortable indoor thermal environment helps occupants work healthily and efficiently, and the air-conditioning system, as the most energy-consuming system in building operation, accurate energy modeling while balancing thermal comfort can provide effective control strategies for managers. Metaheuristic algorithms are widely optimized for various types of machine learning models for the study of data-driven. However, the majority of them currently use the default hyperparameter or random and grid search, which are prone to obtaining inaccurate prediction results. In this paper, a sizable commercial building in Shaanxi Province is used as a research sample. The regional per floor firstly is divided with business model distribution, the cross-validation of random forest and correlation analysis is used to extract features, and the input factors of the prediction model are decided, and then the fully elman neural network is optimized using a segmental fusion strategy combining Cauchy operator and reverse learning to promote harris hawks optimization algorithm, and the prediction model the IMFHHO-FENN (improved Harris Hawks Optimization with multi-strategy fusion-Fully Elman Neural Network) is trained and constructed, which is finally used for predicting thermal comfort and cooling load. According to verification results, it can be proved that the IMFHHO-FENN model has excellent performance compared to mainstream single and combined models and can provide accurate results of predicted models in the design phase of building energy efficiency and later operation management, reducing energy consumption waste while satisfying personnel thermal comfort as much as possible and supply marked theoretical and decision reference for building equipment management.

Stability Analysis of a Multi-area Renewable System and Frequency Control with Improved Chaotic Harris Hawk Optimization Algorithm

Stability Analysis of a Multi-area Renewable System and Frequency Control with Improved Chaotic Harris Hawk Optimization Algorithm

Three Chaotic Strategies for Enhancing the Self-Adaptive Harris Hawk Optimization Algorithm for Global Optimization

Harris Hawk Optimization (HHO) is a well-known nature-inspired metaheuristic model inspired by the distinctive foraging strategy and cooperative behavior of Harris Hawks. As with numerous other algorithms, HHO is susceptible to getting stuck in local optima and has a sluggish convergence rate. Several techniques have been proposed in the literature to improve the performance of metaheuristic algorithms (MAs) and to tackle their limitations. Chaos optimization strategies have been proposed for many years to enhance MAs. There are four distinct categories of Chaos strategies, including chaotic mapped initialization, randomness, iterations, and controlled parameters. This paper introduces SHHOIRC, a novel hybrid algorithm designed to enhance the efficiency of HHO. Self-adaptive Harris Hawk Optimization using three chaotic optimization methods (SHHOIRC) is the proposed algorithm. On 16 well-known benchmark functions, the proposed hybrid algorithm, authentic HHO, and five HHO variants are evaluated. The computational results and statistical analysis demonstrate that SHHOIRC exhibits notable similarities to other previously published algorithms. The proposed algorithm outperformed the other algorithms by 81.25%, compared to 18.75% for the prior algorithms, by obtaining the best average solutions for 13 benchmark functions. Furthermore, the proposed algorithm is tested on a real-life problem, which is the maximum coverage problem of Wireless Sensor Networks (WSNs), and compared with pure HHO, and two well-known algorithms, Grey Wolf Optimization (GWO) and Whale Optimization Algorithm (WOA). For the maximum coverage experiments, the proposed algorithm demonstrated superior performance, surpassing other algorithms by obtaining the best coverage rates of 95.4375% and 97.125% for experiments 1 and 2, respectively.

An enhanced decision-making framework for predicting future trends of sharing economy.

This work aims to provide a reliable and intelligent prediction model for future trends in sharing economy. Moreover, it presents valuable insights for decision-making and policy development by relevant governmental bodies. Furthermore, the study introduces a predictive system that incorporates an enhanced Harris Hawk Optimization (HHO) algorithm and a K-Nearest Neighbor (KNN) forecasting framework. The method utilizes an improved simulated annealing mechanism and a Gaussian bare bone structure to improve the original HHO, termed SGHHO. To achieve optimal prediction performance and identify essential features, a refined simulated annealing mechanism is employed to mitigate the susceptibility of the original HHO algorithm to local optima. The algorithm employs a mechanism that boosts its global search ability by generating fresh solution sets at a specific likelihood. This mechanism dynamically adjusts the equilibrium between the exploration and exploitation phases, incorporating the Gaussian bare bone strategy. The best classification model (SGHHO-KNN) is developed to mine the key features with the improvement of both strategies. To assess the exceptional efficacy of the SGHHO algorithm, this investigation conducted a series of comparative trials employing the function set of IEEE CEC 2014. The outcomes of these experiments unequivocally demonstrate that the SGHHO algorithm outperforms the original HHO algorithm on 96.7% of the functions, substantiating its remarkable superiority. The algorithm can achieve the optimal value of the function on 67% of the tested functions and significantly outperforms other competing algorithms. In addition, the key features selected by the SGHHO-KNN model in the prediction experiment, including " Form of sharing economy in your region " and " Attitudes to the sharing economy ", are important for predicting the future trends of the sharing economy in this study. The results of the prediction demonstrate that the proposed model achieves an accuracy rate of 99.70% and a specificity rate of 99.38%. Consequently, the SGHHO-KNN model holds great potential as a reliable tool for forecasting the forthcoming trajectory of the sharing economy.

Fusion of powerful features of Harris Hawks with Cuckoo Search algorithm for engineering applications

The High-level synthesis (HLS) is a key step in the digital VLSI circuit design. HLS can be seen in the present study as an important matter of intelligently map a Data Flow Graph (DFG) specification of digital filters to FPGA. In this article, the powerful features of the Harris hawks optimization (HHO) and Cuckoo search (CS) algorithm have improved the performance of the proposed method. This proposed strategy is also known as the GChO algorithm. In GChO, the phase of CS helps to force the HHO to escape from stagnation, to ignore local optima and premature convergence. In addition for HLS, the three retiming-based models were also proposed in the work. The standard pipelining was employed in Model 1. The modified Min-Period Retiming on the multiplier less digital filters was incorporated by Model 2 and led to a significant improvement in clock frequency. Model 3 adopted modified Min-Area Retiming on digital filters and reduced the complexity of the circuit; however these designs therefore offer single parameter-based heuristic solution. This can be resolved by using meta-heuristic approach focused on retiming. The use of meta-heuristic methods and swarm intelligence has been considered as a highly desirable choice when searching the digital block solution space with higher frequency and reduced complexity in HLS problems. The robustness of the GChO method has been verified on IEEE CEC’ 2020 standard benchmark test suites. The paper analyzes that the performance of the proposed GChO algorithm outperforms the Harris hawks optimization (HHO), Moth Flame optimization algorithm, Particle Swarm optimization algorithm, Chimp optimization algorithm for the HLS digital filters. The experimental results show that the proposed Model 2 and 3 for retimed PDR-FIR filter is improved in term of MUF by 7.38% and utilized slices by 37.77%, PRO-DFII IIR retimed filter by 10.54%, and complexity reduced by 29.69%, PRO-LAT-ARF by 41.61% and slice by 11.17% and PRO-LATLAD-IIR improved in term of MUF, slices by 35.69% and 38.095% in relative to the standard retimed filters of various architecture. For the evolutionary GChO-retimed filter, an improvement in MUF by 58.52%, and utilized slices by 49.61%, for PDR-FIR, 48.09%, 80.76% for PRO-DFII-IIR , 52.68%, 54.33% for PRO-LAT-ARF, 27.85% and 18.46% for PRO-LATLAD-IIR are achieved in respect of Model 2 and 3 whereas an improvement in term of MUF by 34.60%, and utilized slices by 37.61%, for PDR-FIR, 40.55%, 39.66% for PRO-DFII-IIR, 40.08%, 31.33% for PRO-LAT-ARF, 26.95%, 24.41% for PRO-LATLAD-IIR are achieved with respect of other meta-heuristic methods.

Efficient NOMA system: hybrid heuristic-based network parameter optimization for spectral and energy efficiency with QoS maximization

Abstract Due to its ability to boost the spectral efficiency of wireless communications systems, non-orthogonal multiple access (NOMA) has been deemed promising. NOMA retains the necessary effectiveness to enable 5G communication. The wireless network’s spectral efficiency and energy are reduced due to the limited spectrum and rising demands of users. Because of the mutual cross-tier interference that occurs in heterogeneous networks, NOMA presents brand-new technical difficulties in resource allocation. The use of non-orthogonal resources and spectrum sharing can cause interference that lowers the performance. Therefore, incorporating quality-of-service (QoS) into the design of a new NOMA model with improved bandwidth efficiency and energy efficiency (EE) is absolutely necessary. A deep learning strategy for maximizing the efficiency of spectrum and energy with QoS in NOMA is presented in this paper. In order to increase the efficiency of spectrum and energy with QoS in the NOMA system, an adaptive artificial rabbits Harris Hawks optimization (AARHHO) algorithm is developed to optimize parameters such as the time allocation ratio and beam forming vectors presented in the full-duplex (FD) relay and base station (BS). As a result, the NOMA network efficiency of bandwidth and energy is effectively maximized with QoS using the newly developed AARHHO approach.

Influence maximization in social networks based on discrete harris hawks optimization algorithm

Influence maximization in social networks based on discrete harris hawks optimization algorithm

An Improved Harris Hawks Optimization Algorithm and Its Application in Grid Map Path Planning.

Aimed at the problems of the Harris Hawks Optimization (HHO) algorithm, including the non-origin symmetric interval update position out-of-bounds rate, low search efficiency, slow convergence speed, and low precision, an Improved Harris Hawks Optimization (IHHO) algorithm is proposed. In this algorithm, a circle map was added to replace the pseudo-random initial population, and the population boundary number was reduced to improve the efficiency of the location update. By introducing a random-oriented strategy, the information exchange between populations was increased and the out-of-bounds position update was reduced. At the same time, the improved sine-trend search strategy was introduced to improve the search performance and reduce the out-of-bound rate. Then, a nonlinear jump strength combining escape energy and jump strength was proposed to improve the convergence accuracy of the algorithm. Finally, the simulation experiment was carried out on the test function and the path planning application of a 2D grid map. The results show that the Improved Harris Hawks Optimization algorithm is more competitive in solving accuracy, convergence speed, and non-origin symmetric interval search efficiency, and verifies the feasibility and effectiveness of the Improved Harris Hawks Optimization in the path planning of a grid map.

Coal and Gas Outburst Prediction Model Based on Miceforest Filling and PHHO–KELM

Coal and gas outbursts are some of the most serious coal mine disasters, and effective prediction of coal and gas outbursts can reduce the likelihood of accidents and fatalities. Previously conducted studies have established that machine learning has achieved results in the prediction of coal and gas outbursts, but there is a problem that the available accident data of coal and gas outbursts are diminished or missing. This paper proposes a prediction model based on multiple filling of chained equations for random forests (miceforest) and the Harris Hawk optimization algorithm with Piecewise chaos mapping (PHHO) to optimize the kernel extreme learning machine (KELM) to solve the problem of missing data in coal and gas outburst prediction and to improve prediction accuracy in the case of missing data. Firstly, the miceforest algorithm was adopted to fill missing values in the salient samples, and then the PHHO algorithm was used to optimize the parameters of KELM. Finally, the datasets before and after filling were input into the PHHO–KELM model for experimentation and comparison with other models. The results show that miceforest filling is effective in improving the salient sample accuracy and overall accuracy of predictions, but the improvement is not significant for non-salient samples. The use of the PHHO–KELM model can effectively avoid falling into a local optimum and further improve the prediction accuracy of the KELM algorithm. The salient sample accuracy and overall accuracy of the miceforest–PHHO–KELM model prediction are 96.77% and 98.50%. And an effective coal and gas outburst model has been proposed, which is the miceforest–PHHO–KELM model.

Energy Efficient MAC for M2M Communication Using Dynamic TDMA in IoT Network

In the Internet of Things (IoT) context, the relevance of M2M communication increased, creating the need for practical solutions. MTCDs, or Machine Type Communication Devices, frequently encounter issues such as collisions and delays while transmitting data, ensuring network scalability and maintaining the quality of service (QoS) for all devices participating in the communication. To address these challenges, this paper presents the protocol for switching MAC that optimizes energy usage with Dynamic Time Division Multiple Access (EMAC-DTDMA). TheEMAC-DTDMA protocol involves grouping, dynamic MAC switching, and timeslot allocation. The Harris Hawks Optimization (HHO) algorithm is applied to choose the cluster head of the machine type communication devices (MTCH) while the CSMA/CA and CSMA/CARP protocols with different backoff times are used to minimize collisions based on device density, backlog, and active nodes. The timeslots are allocated based on data size and QoS requirements using Dynamic TDMA. The Markov chain model is employed to overcome synchronization issues with traditional TDMA. The EMAC-DTDMA's performance is evaluated through simulation using a network simulator tool, considering access delay, energy usage, collision probability, and throughput.

Modeling of Artificial Intelligence-Based Automated Climate Control with Energy Consumption Using Optimal Ensemble Learning on a Pixel Non-Uniformity Metro System

Climate control in a pixel non-uniformity metro system includes regulating the air, humidity, and temperature quality within metro trains and stations to ensure passenger comfort and safety. The climate control system in a PNU metro system combines intelligent algorithms, energy-efficient practices, and advanced technologies to make a healthy and comfortable environment for passengers while reducing energy consumption. The proposed an automated climate control using an improved salp swarm algorithm with an optimal ensemble learning technique examines the underlying factors, including indoor air temperature, wind direction, indoor air relative humidity, light sensor 1 (wavelength), return air relative humidity, supply air temperature, wind speed, supply air relative humidity, airflow rate, and return air temperature. Moreover, this new proposed technique applies ISSA to elect an optimal set of features. Then, the climate control process takes place using an ensemble learning approach comprising long short-term memory, gated recurrent unit, and recurrent neural network. Lastly, the Harris hawks optimization algorithm can be employed to adjust the hyperparameters related to the ensemble learning models. The extensive results demonstrated the supremacy of the proposed algorithms over other approaches to the climate control process on PNU metro systems.

Feature selection using bio-inspired optimization for IoT intrusion detection and prevention system

Nowadays smart Internet of Things (IoT) devices are used briskly, and these devices communicate with each other via wireless medium. However, this increase in IoT devices has resulted in a rise of security issues associated with the IoT system. Therefore, an intrusion detection and prevention system (IDPS) is used to locate and report any malicious activity. The IDPS's feature selection (FS) task is necessary to improve the data quality and decrease the data used for classifying intrusive traffic. Therefore, this paper proposes a novel FS method that hybridizes improved salp swarm algorithm and harris hawk optimization algorithm. The XGBoost classifier is used for classifying reduced network traffic. Proposed system demonstrates high accuracy and low computation time, surpassing other related approaches used for the IDPS feature selection task.

An efficient load balancing in cloud computing using hybrid Harris hawks optimization and cuckoo search algorithm

An efficient load balancing in cloud computing using hybrid Harris hawks optimization and cuckoo search algorithm

Enhanced Harris hawks optimization-based fuzzy k-nearest neighbor algorithm for diagnosis of Alzheimer's disease

In order to stop deterioration and give patients with Alzheimer's disease (AD) early therapy, it is crucial to correctly diagnose AD and its early stage, mild cognitive impairment (MCI). A framework for diagnosing AD is presented in this paper, which includes magnetic resonance imaging (MRI) image preprocessing, feature extraction, and the Fuzzy k-nearest neighbor algorithm (FKNN) model. In particular, the framework's novelty lies in the use of an improved Harris Hawks Optimization (HHO) algorithm named SSFSHHO, which integrates the Sobol sequence and Stochastic Fractal Search (SFS) mechanisms for optimizing the parameters of FKNN. The HHO method improves the quality of the initial population overall by incorporating the Sobol sequence, and the SFS mechanism increases the algorithm's capacity to get out of the local optimum solution. Comparisons with other classical meta-heuristic algorithms, state-of-the-art HHO variants in low and high dimensions, and enhanced meta-heuristic algorithms on 30 typical IEEE CEC2014 benchmark test problems show that the overall performance of SSFSHHO is significantly better than other comparative algorithms. Moreover, the created framework based on the SSFSHHO-FKNN model is employed to classify AD and MCI using MRI scans from the ADNI dataset, achieving high classification performance for 6 representative cases. Experimental findings indicate that the proposed algorithm performs better than a number of high-performance optimization algorithms and classical machine learning algorithms, thus offering a promising approach for AD classification. Additionally, the proposed strategy can successfully identify relevant features and enhance classification performance for AD diagnosis.

A hybrid model of commercial building cooling load prediction based on the improved NCHHO-FENN algorithm

Accurate building cooling load prediction is one of the key conditions for optimal operation, energy efficiency and sustainability studies of large central air-conditioning systems. In this paper, a novel model based on nonlinear chaotic mapping Harris Hawks Optimization (HHO) optimized Fully Elman Neural Network (FENN), called NCHHO-FENN, is proposed for building cooling load prediction. First, the entropy weight method is combined with the grey correlation method to generate evaluation indexes for feature extraction. Then, parameters of the Fully Elman Neural Network prediction model were optimized using the nonlinear chaotic Harris hawk optimization (NCHHO) algorithm, and the hybrid model for cooling load prediction was constructed. Finally, cool loading data from a sizable commercial building in Shaanxi Province was adopted as the research sample for validation. The experimental results show that the Root Mean Square Error (RMSE) and coefficient of determination (R-square) of the NCHHO-FENN prediction model are reduced and improved by 54.82%–6.64%, 49.20%–4.81% and 11.72%–0.46% respectively, compared with the three improved Fully Elman Neural Network models, which explains that harris hawks algorithm can not only be used to optimize the parameters of fully elman neural network but also has superior prediction effect. At the same time, in the case of a few training samples, NCHHO-FENN can still proficiently predict the cooling load in various months, which also shows that it has high generalization. It demonstrates that the proposed method is efficient and reasonable, and can be applied to building cooling load prediction, contributing to theoretical justification for building energy-saving operation and management.

An agile autonomous car driving assistance using hybrid optimization-based kernel support vector convolutional network

In robotic control systems, autonomous car driving is considered a complicated task. The conventional modular techniques necessitate precise localization, planning, and mapping procedures to provide safe driving. These requirements make autonomous car driving tasks computationally ineffective and sensitive to ecological changes. In recent times, the emergence of deep learning-based approaches in autonomous car driving systems provided promising solutions for complex vision task interpretation, localization, and environmental perception. But, they create distribution mismatch issues and require huge perilous interaction information to offer safe driving with the prediction of future collisions. Therefore, to overcome this issue, a novel Modified Kernel Support vector Convolutional based Hybrid Grasshopper Harris hawk (MKSC-HGH) approach is proposed. The proposed MKSC-HGH approach efficiently forecasts the uncertainties of agile autonomous car driving and guides proper navigation details of the track to avoid the possibility of collision with on-road obstacles and fences. This system inputs camera images captured through sensors, car speed, and manual driving data to analyze and predict safety control and probable collision. The prominent feature representing the ability of Modified Convolutional Neural Network (MCNN) and complex boundary encoding process of Kernel Support Vector Machine (KSVM) with hyperparameter tuning operation of Modified Grasshopper Optimization Algorithm based Improved Harris Hawk Optimization (MGOA-IHHO) algorithm made proposed system applicable to predict future states (ie. visual prediction, speed prediction, and collision prediction) more accurately. The efficiency of the proposed MKSC-HGH approach is investigated using evaluation measures namely completion time, average speed, top speed, and laps time. The simulation outcome shows the superiority of the proposed MKSC-HGH approach in predicting agile autonomous car driving over state of art techniques about task performance and sample efficiency.

Efficient Harris Hawk Optimization (HHO)-Based Framework for Accurate Skin Cancer Prediction

The prediction of skin cancer poses a number of challenges due to the differences in visual characteristics between melanoma, basal cell carcinomas, and squamous cell carcinomas. These visual differences pose difficulties for models in discerning subtle features and patterns accurately. However, a remarkable breakthrough in image analysis using convolutional neural networks (CNNs) has emerged, specifically in the identification of skin cancer from images. Unfortunately, manually designing such neural architectures is prone to errors and consumes substantial time. It has become increasingly popular to design and fine-tune neural networks by using metaheuristic algorithms that are based on natural phenomena. A nature-inspired algorithm is a powerful alternative to traditional algorithms for solving problems, particularly in complex optimization tasks. One such algorithm, the Harris hawk optimization (HHO), has demonstrated promise in automatically identifying the most appropriate solution across a wide range of possibilities, making it suitable for solving complex optimization problems. The purpose of this study is to introduce a novel automated architecture called “HHOForSkin” that combines the power of convolutional neural networks with meta-heuristic optimization techniques. The HHOForSkin framework uses an innovative custom CNN architecture with 26 layers for the analysis of medical images. In addition, a Harris hawk optimization algorithm (HHO) is used to fine-tune the developed model for multiple skin cancer classification problems. The developed model achieves an average accuracy of 99.1% and 98.93% F1 score using a publicly available skin cancer dataset. These results position the developed optimization-based skin cancer detection strategy at the forefront, offering the highest accuracy for seven-class classification problems compared to related works.