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INTERNATIONAL JOURNAL OF MATHEMATICAL ENGINEERING AND MANAGEMENT SCIENCES 2021年第4期6卷 1157-1173页

作者： Al-Bazoon, Mustafa Univ Misan Dept Civil Engn Coll Engn Amarah Maysan Iraq

This article investigates the use of harris hawks optimization (HHO) to solve planar and spatial trusses with design variables that are discrete. The original HHO has been used to solve continuous design variables problems. However, HHO is formulated to solve optimization problems with discrete variables in this research. HHO is a population-based metaheuristic algorithm that simulates the chasing style and the collaborative behavior of predatory birds harris hawks. The mathematical model of HHO uses a straightforward formulation and does not require tuning of algorithmic parameters and it is a robust algorithm in exploitation. The performance of HHO is evaluated using five benchmark structural problems and the final designs are compared with ten state-of-the-art algorithms. The statistical outcomes (average and standard deviation of fmal designs) show that HHO is quite consistent and robust in solving truss structure optimization problems. This is an important characteristic that leads to better confidence in the final solution from a single run of the algorithm for an optimization problem.

关键词： harris hawks optimization algorithm Discrete structural optimization optimization of truss structures

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A Novel Fuzzy Based Solar Irradiation Prediction Approach for Selection of Solar Panels in Hisar (Haryana)

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CYBERNETICS AND SYSTEMS 2023年第6期54卷 914-937页

作者： Singh, Vijay Pal Arya, Sandeep Kumar Shankar, Ajay Guru Jambheshwar Univ Sci & Technol Dept Elect & Commun Engn Hisar Haryana India Guru Jambheshwar Univ Sci & Technol Dept Phys Hisar Haryana India

This paper proposes an ANN-based framework for forecasting the daily solar irradiance with the meteorological data collected over 10 years for Hisar city, located in Haryana. Meteorological information like temperature, relative humidity, and average wind speed is used as input variables for estimating sun irradiation in order to obtain high accuracy under various weather situations. Meteorological information is used as input variables for estimating the solar irradiation in order to obtain acceptable accuracy in a variety of weather circumstances. In addition, it was suggested in this paper that the Artificial Neural Network (ANN) model be trained using the harris hawks optimization (HHO) algorithm. The following task is to choose the best solar panel (SP) out of the thousands that are currently on the market. Due to a crucial component like weather, choosing the finest SP is important. Hence, this paper introduces a score-based model for selecting the best panel by considering the different criteria of the eleven SPs that are used as test panels. The annual power produced by panel P11 is 79.67% better than panel P1. Finally, based on the average power generated, the proposed panel P11 is regarded as the best panel when compared to other panels.

关键词： harris hawks optimization algorithm neural network solar irradiation prediction solar panels

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A New Reconfigured Electricity Market Bidding Strategy in View of Players' Concerns

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IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS 2022年第6期58卷 7034-7046页

作者： Abedinia, Oveis Ghasemi-Marzbali, Ali Nurmanova, Venera Bagheri, Mehdi Nazarbayev Univ Dept Elect & Comp Engn Nur Sultan 010000 Kazakhstan Mazandaran Univ Sci & Technol Dept Elect & Biomed Engn Babol *** Iran

The electricity market has become a competitive model due to generation, transmission, distribution, and large-scale investments in microgrids where no electricity supplier intends to set the price. However, the competition itself provides opportunities for the buyer to be able to experience more profit. Based on the available optimal bidding model, the supplier can only offer the final bid. Hence, active participation on the demand side creates an efficient and reasonable electricity market, and also promotes additional optimal allocation of economic resources. This article has concentrated over the final marginal bidding price and creating an optimal bidding strategy for consumers. It has specifically focused and proposed a new optimized model for electricity market bidding to improve and obtain a maximum turnover and profit for players. Since the solution of the aforementioned issue is inadequate due to multiple decision variables in mathematical and classical methods;a new enhanced harris hawks optimization (HHO) is proposed. Trapping into local optimum point can be occurred in different optimization algorithms, while the proposed model has simple and efficient structure, without derivative operator, which is integrated with mathematical techniques to increase local and global search abilities. The standard HHO algorithm is utilized to increase the probability of entrapment in local optima through increasing the number of optimization variables;however, the challenge of large number of variables is resolved through proposing a novel local/global operators. The proposed model is examined with a standard IEEE system in 2-scenarios, with symmetric and asymmetric information through finding the optimal solution desirably.

关键词： Auction bidding optimization competitive electricity market electricity bidding strategy electricity market harris hawks optimization algorithm

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An efficient harris hawk optimization algorithm for solving the travelling salesman problem

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2022年第3期25卷 1981-2005页

作者： Gharehchopogh, Farhad Soleimanian Abdollahzadeh, Benyamin Islamic Azad Univ Urmia Branch Dept Comp Engn Orumiyeh Iran

Travelling Salesman Problem (TSP) is an Np-Hard problem, for which various solutions have been offered so far. Using the harris Hawk optimization (HHO) algorithm, this paper presented a new method that uses random-key encoding to generate a tour. This method helps maintain the main capabilities of the HHO algorithm, on the one hand, and to take advantage of the capabilities of active mechanisms in the continuous-valued problem space on the other hand. For the exploration phase, the DE/best/2 mutation mechanism employed in the exploitation phase, besides the main strategies in the HHO algorithm, was used. Ten neighborhood search operators are used, four of which are introduced. These operators were intelligently selected using the MCF. The Lin-Kernighan local search mechanism was utilized to improve the proposed algorithm's performance, and the Metropolis acceptance strategy was employed to escape the local optima trap. Besides, 80 datasets were evaluated in TSPLIB to demonstrate the performance and efficiency of the proposed algorithm. The results showed the excellent performance of the proposed algorithm.

关键词： harris hawks optimization algorithm Travelling salesman problem optimization Neighbourhood search operator

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Workload Prediction Based Virtual Machine Migration and Optimal Switching Strategy for Cloud Power Management

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WIRELESS PERSONAL COMMUNICATIONS 2022年第1期123卷 761-784页

作者： Pushpalatha, R. Ramesh, B. Vishveshwaraya Technol Univ VTU PG Ctr Ring Rd Mysore 570019 Karnataka India Malnad Coll Engn Hassan 573202 Karnataka India

Virtual Machine (VM) Migration has been popular nowadays, as it helps to balance the load effectively. Various VM migration-based approaches are modeled for better VM placement but remain the challenge because of inappropriate load balancing. Thus, workload prediction-based VM migration is introduced to improve the energy efficiency of the system. Importantly, load prediction is very important to enhance resource allocation and utilization. Chaotic Fruitfly Rider Neural Network is devised by combining Rider neural network and chaotic Fruitfly optimization algorithm to predict load. Moreover, the fitness for predicting the load is based on old-time load, resource constraint, and network parameters. Once the load is predicted, the power optimization is performed using VM migration and optimal switching strategy. When the load is found overloaded, the VM migration is performed using the proposed harris hawks spider monkey optimization (HHSMO). Thus, the optimal finding of VM for executing the removed task is found out using the proposed HHSMO. The fitness function utilized for the VM migration is based on power, load, and resource parameter. If the load predicted is underloaded, the optimal switch ON/OFF is done optimally by switch ON/OFF the servers using the proposed HHSMO algorithm. Through the migration and switching strategy, the power consumption is optimized. The performance of the proposed model is evaluated in terms of power consumption, load, and resource utilization. The proposed HHSMO achieves the minimal power consumption of 0.0181, the minimal load of 0.002, and the minimal resource utilization of 0.0376.

关键词： Workload prediction Power consumption Chaotic fruitfly optimization algorithm Rider neural network harris hawks optimization algorithm

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Explainable prediction of deposited film thickness in IC fabrication with CatBoost and SHapley Additive exPlanations (SHAP) models

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APPLIED INTELLIGENCE 2024年第1期54卷 246-263页

作者： Shi, Yumeng Cai, Yu Lou, Shunyuan Chen, Yining Zhejiang Univ Sch Micronano Elect 733 Jianshe 3rd Rd Hangzhou 311200 Zhejiang Peoples R China Univ Sci & Technol China Sch Microelect 100 Fuxing Rd Hefei 230031 Anhui Peoples R China

This paper presents a study on developing a chemical vapor deposition film thickness prediction model for semiconductor IC manufacturing. Traditional methods for measuring layer thickness are time-consuming and labor-intensive, prompting the exploration of machine-learning techniques for virtual measurement. These techniques estimate layer thickness directly from production process data, enabling real-time monitoring of product quality and maintaining process efficiency. This study used the harris hawks optimization algorithm (HHO) to perform feature selection on deposition process data and constructed a predictive model using the selected feature parameters. Four machine learning models, namely Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost), are evaluated using metrics such as mean squared error (MSE), mean absolute percentage error (MAPE), and R-squared (R-2) on a test set. Results demonstrate that the CatBoost model outperforms the others with an MSE of 353.55, MAPE of 0.246% and R-2 of 0.796, highlighting its superior prediction accuracy in estimating film thickness. However, the integrated tree model used in this study is complex, and its prediction results cannot be directly interpreted, making the model less credible. To address this issue, the SHAP method was introduced for interpretable analysis of the model. Overall, this study shows that the use of machine learning models, such as the CatBoost model, combined with SHAP analysis, significantly improves the credibility of prediction results on deposited film thickness in IC fabrication. Moreover, the timely adjustment of production equipment parameters can be achieved to reduce process deviations and improve IC yield.

关键词： IC deposition film thickness SHAP explanation CatBoost Machine learning harris hawks optimization algorithm

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Model Predictive Controller-Based Voltage and Frequency Regulation in Renewable Energy Integrated Power System Coordinated with Virtual Inertia and Redox Flow Battery

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF ELECTRICAL ENGINEERING 2023年第1期47卷 159-176页

作者： Kumar, Vineet Sharma, Veena Naresh, R. Natl Inst Technol Hamirpur Elect Engn Dept Hamirpur 177005 Himachal Prades India

This manuscript discusses the concurrent voltage and frequency control problem for an interconnected system having three areas, each containing thermal and diesel units in conjunction with renewable integration from a separate source. In the first case study, renewable sources like wind, solar, and geothermal have been incorporated using a static transfer function-based model without considering variability in wind and solar. In the second case study, the dynamic model of wind and solar has been considered after incorporating the deterministic drifts and stochastic fluctuations. The paper presents harris hawks optimized model predictive controller (MPC-HHO) to curtail the frequency and voltage fluctuations caused due to the sudden change in loading pattern. Additionally, the authors have incorporated various dynamic energy storage devices like virtual inertia and redox flow battery (VI-RFB) in coordination with the MPC-HHO controller. From the obtained results, it has been validated that the transient response from the proposed MPC-HHO controller coordinated with VI-RFB has superior characteristics when compared with state-of-the-art methodologies available in the existing literature. Further, the most prevalent nonlinearities present in the realistic power system have been considered and their effects on the controller's performance have been investigated and the robustness of the proposed MPC-HHO controller coordinated with VI-RFB has been successfully validated.

关键词： Automatic voltage control harris hawks optimization algorithm Load frequency control Model predictive controller

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Investigation of Performance of HHO algorithm in Solving Global optimization Problems

Investigation of Performance of HHO Algorithm in Solving Glo...

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International Conference on Artificial Intelligence and Data Processing (IDAP)

作者： Eker, Erdal Kayri, Murat Ekinci, Serdar Mus Alparslan Univ Dept Mkt & Advertising Mus Turkey Batman Univ Dept Comp Engn Batman Turkey

ISBN: (纸本)9781728129334;9781728129327

In recent years, the use of meta-heuristic optimization algorithms has been increasing in most areas of science and engineering. These algorithms have advantages and disadvantages to each other. In this study, the performance of the harris hawks optimization (HHO) algorithm has been verified by performing comparative statistical analysis of the optimal solutions of some well-known benchmark functions. Sphere, Rosenbrock, Schwefel, Ackley, Egg Crate and Easom are the chosen benchmark functions that are commonly used. From the analysis results, it is seen that the HHO algorithm were superior to artificial bee colony (ABC), wind driven optimization (WDO) and atom search optimization (ASO) algorithms. In addition, the results of the statistical boxplot prove the unique performance and efficiency of the HHO algorithm.

关键词： harris hawks optimization algorithm optimization benchmark functions meta-heuristic algorithms

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Research on UAV access deployment algorithm based on improved virtual force model

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2022年第8期16卷 2606-2626页

作者： Zhang, Shuchang Wu, Duanpo Jiang, Lurong Jin, Xinyu Cen, Shuwei Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China Zhejiang Prov Key Lab Informat Proc Commun & Netwo Hangzhou 310027 Peoples R China Zhejiang Sci Tech Univ Sch Informat Sci & Technol Hangzhou 310018 Peoples R China Zhejiang Univ Dept Informat Sci & Elect Engn Hangzhou 310027 Peoples R China China Mobile Commun Grp Zhejiang Co Ltd Hangzhou Branch Hangzhou 310006 Peoples R China

In this paper, a unmanned aerial vehicle (UAV) access deployment algorithm is proposed, which is based on an improved virtual force model to solve the poor coverage quality of UAVs caused by limited number of UAVs and random mobility of users in the deployment process of UAV base station. First, the UAV-adapted harris hawks optimization (U-AHHO) algorithm is proposed to maximize the coverage of users in a given hotspot. Then, a virtual force improvement model based on user perception (UP-VFIM) is constructed to sense the mobile trend of mobile users. Finally, a UAV motion algorithm based on multi-virtual force sharing (U-MVFS) is proposed to improve the ability of UAVs to perceive the moving trend of user equipments (UEs). The UAV independently controls its movement and provides follow-up services for mobile UEs in the hotspot by computing the virtual force it receives over a specific period. Simulation results show that compared with the greedy-grid algorithm with different spacing, the average service rate of UEs of the U-AHHO algorithm is increased by 2.6% to 35.3% on average. Compared with the baseline scheme, using UP-VFIM and U-MVFS algorithms at the same time increases the average of 34.5% to 67.9% and 9.82% to 43.62% under different UE numbers and moving speeds, respectively.

关键词： UAV deployment virtual force harris hawks optimization algorithm user perception

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Hand gesture recognition based on a harris hawks optimized Convolution Neural Network

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COMPUTERS & ELECTRICAL ENGINEERING 2022年 100卷

作者： Gadekallu, Thippa Reddy Srivastava, Gautam Liyanage, Madhusanka Iyapparaja, M. Chowdhary, Chiranji Lal Koppu, Srinivas Maddikunta, Praveen Kumar Reddy Vellore Inst Technol Sch Informat Technol & Engn Vellore Tamil Nadu India Brandon Univ Dept Math & Comp Sci Brandon MB Canada Univ Coll Dublin Sch Comp Sci Dublin Ireland Univ Oulu Ctr Wireless Commun Oulu Finland China Med Univ Res Ctr Interneural Comp Taichung Taiwan

Hand gestures are an effective method of communication, especially when we are communicating with people who cannot understand our spoken language. Furthermore, it is a key aspect to human-computer interaction. Understanding hand gestures is very important to ensure that listeners understand what speakers are attempting to communicate. Even though several researchers have proposed deep learning-based models for hand gesture recognition, the hyper-parameter tuning of these models is a relatively unexplored area. In this work, Convolutional Neural Networks (CNN) are used to classify hand gesture images. To tune the hyper-parameters of the CNN, a recently developed metaheuristic algorithm, namely, the harris hawks optimization (HHO) algorithm, is used. Our in-depth comparative analysis proves that the proposed HHO-CNN hybrid model outperforms the existing models by attaining an Accuracy of 100%.

关键词： Hand gesture classification Convolutional Neural Networks harris hawks optimization algorithm Image classification

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