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Improved hho algorithm based on good point set and nonlinear convergence formula

The Journal of China Universities of Posts and Telecommunications 2021年第2期28卷 48-67页

作者： Guo Hairu Meng Xueyao Liu Yongli Liu Shen School of Computer Science and Technology Henan Polytechnic UniversityJiaozuo 454000China

Harris hawks optimization(hho)algorithm is an efficient method of solving function optimization ***,it is still confronted with some limitations in terms of low precision,low convergence speed and stagnation to local *** this end,an improved hho(Ihho)algorithm based on good point set and nonlinear convergence formula is ***,a good point set is used to initialize the positions of the population uniformly and randomly in the whole search ***,a nonlinear exponential convergence formula is designed to balance exploration stage and exploitation stage of Ihho algorithm,aiming to find all the areas containing the solutions more comprehensively and *** proposed Ihho algorithm tests 17 functions and uses Wilcoxon test to verify the *** results indicate that Ihho algorithm not only has faster convergence speed than other comparative algorithms,but also improves the accuracy of solution effectively and enhances its robustness under low dimensional and high dimensional conditions.

关键词： hho algorithm local optimum good point set nonlinear formula multi-dimension

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

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年第5期49卷 6531-6550页

作者： Pati, Subhranshu Sekhar Subudhi, Umamani Int Inst Informat Technol Dept Elect Engn Bhubaneswar 751003 India

Smart grids are the electrical grid with intelligent communication systems comprising conventional- and renewable-based power generation units along with storage systems that can monitor the power flow from generation to consumption and manage power flow to match the generation. This paper studies the frequency control of multi-area systems integrated with traditional and renewable power plants taking reliability and sustainability into account. Several nonlinearities like boiler dynamics and generation rate constant are also reflected in the simulation model. Further, a delay is introduced in the plant and controller formulation phase. To maintain stability, fractional calculus-based Proportional-Integral-derivative controller is formulated. The gain parameters of the fractional controller are optimized using the recent optimization algorithm. Furthermore, the chaotic-based function is also included in the existing Harris hawk optimizer to improve the optimizer's effectiveness. System stability is established with the fractional controller through the Bode plot and eigenvector analysis. Additionally, the system response is evaluated with the application of 2% step loading and time-varying random loading. The system's response through the suggested controller is verified using MATLAB/SIMULINK environment with various widely used controllers, and significant performance enhancement of peak overshoot, undershoot, and settling time are also observed.

关键词： Stability analysis Load frequency control Renewable source hho algorithm iChho algorithm FOPID controller

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Performance improvement of aircraft pitch angle control using a new reduced order fractionalized PID controller

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ASIAN JOURNAL OF CONTROL 2023年第4期25卷 2588-2603页

作者： Idir, A. Bensafia, Y. Khettab, K. Canale, L. Univ Msila Dept Elect Engn Msila 28000 Algeria Univ Boumerdes Appl AutomatLab FHC Boumerdes 35000 Algeria Bouira Univ Dept Elect Engn Bouira 10000 Algeria CNRS LAPLACE UMR 5213 Toulouse France

In this paper, a new optimal reduced order fractionalized PID (ROFPID) controller based on the Harris Hawks Optimization algorithm (hhoA) is proposed for aircraft pitch angle control. Statistical tests, analysis of the index of performance, and disturbance rejection, as well as transient and frequency responses, were all used to validate the effectiveness of the proposed approach. The performance of the proposed hhoA-ROFPID and hhoA-ROFPID controllers with Oustaloup and Matsuda approximations was then compared not only to the PID controller tuned by the original hho algorithm but also to other controllers tuned by cutting-edge meta-heuristic algorithms such as the atom search optimization algorithm (ASOA), Salp Swarm algorithm (SSA), sine-cosine algorithm (SCA), and Grey wolf optimization algorithm (GOA). Simulation results show that the proposed controller with the Matsuda approximation provides better and more robust performance compared to the proposed controller with the Oustaloup approximation and other existing controllers in terms of percentage overshoot, settling time, rise time, and disturbance rejection.

关键词： fractionalized PID hho algorithm PID control pitch angle control reduced order approximation

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ICS Deployment Optimization with Time Delay Reduction and Energy-Efficiency in IWSNs 25

ICS Deployment Optimization with Time Delay Reduction and En...

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IEEE Wireless Communications and Networking Conference (IEEE WCNC)

作者： Aslam, Muhammad Muzamil Tufail, Ali Apong, Rosyzie Anna Awg Haji Mohd De Silva, Liyanage Chandratilak Kalinaki, Kassim Namoun, Abdallah Univ Brunei Darussalam Sch Digital Sci Gadong Brunei Islamic Univ Uganda IUIU Dept Comp Sci Mbale Uganda Islamic Univ Madinah Fac Comp & Informat Syst Madinah 42351 Saudi Arabia

ISBN: (纸本)9798350303582;9798350303599

Significant time delays and energy inefficiencies are frequently caused by implementing Industrial Control Systems (ICS) in Industrial Wireless Sensor Networks (IWSN). To overcome these issues, this paper suggests two optimization techniques: Harris Hawk Optimization (hho) and Gradient-Based Optimization (GBO). The GBO technique uses gradient data to optimize ICS deployment to reduce delay times and increase IWSN energy efficiency. The system achieves an efficient communication flow and minimizes transmission delays by incrementally altering the arrangement of ICS components based on gradient descent. The allocation of resources and the elimination of unnecessary data transmissions optimize energy use. Similarly, the hho approach optimizes ICS deployment in terms of time delay reduction and energy economy by modeling the cooperative hunting approach of Harris Hawks. The hho algorithm effectively balances exploration and exploitation to find the best deployment configurations. Extensive simulations assess the proposed method in simulation model ICS scenarios. The outcomes show how well GBO and hho work to cut down on delays and increase energy effectiveness. The enhanced ICS deployment reduces time delays and uses less energy than traditional deployment techniques, improving IWSN performance. The simulation code can be accessed via: [https://***/Muzamil282/hho-for-time-reduction-in-an-ICS-System-]

关键词： IWSN ICS hho algorithm GBO algorithm Energy efficiency

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An Enhanced Ihho-LSTM Model for Predicting Online Public Opinion Trends in Public Health Emergencies

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SAGE OPEN 2024年第2期14卷

作者： Mu, Guangyu Li, Jiaxue Liao, Zehan Yang, Ziye Jilin Univ Finance & Econ Changchun Peoples R China Key Lab Financial Technol Jilin Prov Changchun Peoples R China Jilin Univ Finance & Econ Sch Management Sci & Informat Engn Changchun 130117 Peoples R China

Social networks accelerate information communication in public health emergencies. Some negative information may cause an outbreak of public opinion crisis. Accurately predicting online public opinion trends can help the relevant departments take timely and effective measures to cope with risks. Therefore, this research proposes a prediction model incorporating the swarm intelligence optimization algorithm and the deep learning method. In this model, we improve the Harris Hawks Optimization (hho) algorithm by introducing the Cauchy distribution function, the stochastic contraction exponential function, and the adaptive inertia weight. Then we utilize the improved hho (Ihho) algorithm to optimize the hyperparameters of the deep learning method LSTM, including the learning rate and the number of neurons in the hidden layer. Finally, we construct the Ihho-LSTM model to make predictions in three public health emergencies. The experiments verify that the proposed model outperforms other single and hybrid models. The MAPE values reduce by 78.34%, 54.46%, and 46.42% relative to the average values of the three single models. Compared with the mean values of the two hybrid models, the MAPE values decrease by 47.69%, 18.45%, and 5.78%. The Ihho-LSTM model can be applied to public opinion early warning and reversal identification, providing a reference in public opinion management. [Purpose] Social networks accelerate information communication in public health emergencies. Some negative information may cause an outbreak of public opinion crisis. Accurately predicting online public opinion trends can help the relevant departments take timely and effective measures to cope with risks. [Method] This research proposes a prediction model incorporating the swarm intelligence optimization algorithm and the deep learning method. In this model, we improve the Harris Hawks Optimization (hho) algorithm by introducing the Cauchy distribution function, the stochastic contraction exponentia

关键词： hho algorithm deep learning Ihho-LSTM model online public opinion trend prediction public health emergencies

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Advancements in Power System Stability: FOPID Control Optimization using Harris Hawk algorithms in SMIB Systems 6

Advancements in Power System Stability: FOPID Control Optimi...

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6th IEEE International Conference on Emerging Smart Computing and Informatics, ESCI 2024

作者： Kalidas Kirange, Yogesh Nema, Pragya Oriental University Department of Electrical and Electronics Engineering Indore India

ISBN: (纸本)9798350306613

Ensuring robust power system stability is paramount for the reliable operation of electrical grids. This seminal study delves deeply into the enhancement of stability within Single Machine Infinite Bus (SMIB) configurations by synergistically integrating Fractional Order Proportional Integral Derivative (FOPID) controllers with the Harris Hawks Optimization (hho) algorithm. The SMIB model, emblematic of a synchronous generator interfaced with an infinite bus, serves as the cornerstone for meticulous stability analyses. Integral to this research is the deployment of FOPID controllers, characterized by fractional order parameters encompassing proportional, integral, and derivative facets, pivotal for nuanced control in dynamic grid environments. Leveraging the innate capabilities of hho-a nature-inspired optimization paradigm-the study meticulously refines FOPID parameters. Methodologically, this entails crafting an objective function targeting minimized control discrepancies and employing hho to iteratively hone the FOPID controller's efficacy. Empirical evaluations unveil compelling outcomes: markedly enhanced stability profiles, diminished overshoot phenomena, expedited settling durations, and augmented transient responses. Consequently, this pioneering research unveils a transformative methodology amalgamating fractional order control nuances with nature-inspired optimization techniques, heralding a promising trajectory toward fortifying power system stability in contemporary electrical infrastructures. © 2024 IEEE.

关键词： FOPID controllers hho algorithm Power System Stability SMIB System Transient Response

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Henry Hub monthly natural gas price forecasting using CEEMDAN-Bagging-hho-SVR

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Duan, Yonghui Zhang, Jianhui Wang, Xiang Henan Univ Technol Dept Civil Engn Zhengzhou Peoples R China Zhengzhou Univ Aeronaut Dept Civil Engn Zhengzhou Peoples R China

As a clean fossil energy source, natural gas plays a crucial role in the global energy transition. Forecasting natural gas prices is an important area of research. This paper aims at developing a novel hybrid model that contributes to the prediction of natural gas prices. We develop a novel hybrid model that combines the "Decomposition algorithm" (CEEMDAN), "Ensemble algorithm" (Bagging), "Optimization algorithm" (hho), and "Forecasting model" (SVR). The hybrid model is used for monthly Henry Hub natural gas forecasting. To avoid the problem of data leakage caused by decomposing the whole time series, we propose a rolling decomposition algorithm. In addition, we analyzed the factors affecting Henry Hub natural gas prices for multivariate forecasting. Experimental results indicate that the proposed model is more effective than the traditional model at predicting natural gas prices.

关键词： natural gas price forecasting Henry Hub SVR CEEMDAN decomposition Bagging hho algorithm

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hho-Based Vector Quantization Technique for Biomedical Image Compression in Cloud Computing

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INTERNATIONAL JOURNAL OF IMAGE AND GRAPHICS 2023年第3期23卷 2240008-2240008页

作者： Kumar, T. Satish Jothilakshmi, S. James, Batholomew C. C. Prakash, M. Arulkumar, N. Rekha, C. BMS Inst Technol & Management Dept CSE Bengaluru India Amer Coll Madurai Tamil Nadu India Angeles Univ Fdn Angeles City Philippines Karpagam Coll Engn Data Sci & Analyt Ctr Coimbatore India CHRIST Deemed Univ Dept CS Bangalore India Sri Sai Ram Inst Technol Dept IT Chennai India

In the present digital era, the exploitation of medical technologies and massive generation of medical data using different imaging modalities, adequate storage, management, and transmission of biomedical images necessitate image compression techniques. Vector quantization (VQ) is an effective image compression approach, and the widely employed VQ technique is Linde-Buzo-Gray (LBG), which generates local optimum codebooks for image compression. The codebook construction is treated as an optimization issue solved with utilization of metaheuristic optimization techniques. In this view, this paper designs an effective biomedical image compression technique in the cloud computing (CC) environment using Harris Hawks Optimization (hho)-based LBG techniques. The hho-LBG algorithm achieves a smooth transition among exploration as well as exploitation. To investigate the better performance of the hho-LBG technique, an extensive set of simulations was carried out on benchmark biomedical images. The proposed hho-LBG technique has accomplished promising results in terms of compression performance and reconstructed image quality.

关键词： Biomedical image processing data compression LBG vector quantization hho algorithm cloud computing

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Hybrid multi-objective Harris Hawk optimization algorithm based on elite non-dominated sorting and grid index mechanism

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ADVANCES IN ENGINEERING SOFTWARE 2022年第0期172卷

作者： Wang, Min Wang, Jie-Sheng Song, Hao-Ming Zhang, Min Zhang, Xing-Yue Zheng, Yue Zhu, Jun-Hua Univ Sci & Technol Liaoning Sch Elect & Informat Engn Anshan 114044 Peoples R China

In order to find Pareto optimal solution set uniformly distributed along all objectives, a Hybrid Multi-Objective Harris Hawk Optimization algorithm (H-MOhho) was proposed based on elite non-dominated sorting and grid indexing mechanism. In order to maintain and improve the coverage of Pareto optimal solution, a method combining the two terms is adopted to obtain the optimal Pareto optimal solution set. Firstly, a non-dominated ranking mechanism based on elite was used to assign rank and sum to select the best solution set, and then the archived grid index mechanism with update mechanism was used to select the final solution set. This hybrid structure can not only obtain the optimal Pareto solution set but also keep the diversity of the population and improve the effectiveness of solving multi-objective optimization problems. In order to verify the performance of the proposed H-MOhho algorithm, 22 test functions and 4 multi-objective engineering problems are used for simulation, and four performance indexes are compared with Multi-Objective Particle Swarm Optimization (MOPSO), Non-dominated Sorting Genetic algorithm II (NSGA-II), Multi-Objective Ant Lion Optimizer (MOALO), Multi-Objective Salp Swarm algorithm (MSSA) and Multi-Objective Dragonfly algorithm (MODA). Experimental results show that the proposed H-MOhho algorithm has better competitiveness and applicability.

关键词： Multi -objective optimization Pareto front hho algorithm Elite non -dominant sorting Grid indexing mechanism

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Optimal sustainable order quantities for growing items

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JOURNAL OF CLEANER PRODUCTION 2021年 307卷 127216-127216页

作者： Gharaei, Abolfazl Almehdawe, Eman Univ Regina Fac Business Adm Regina SK Canada

Some industries, such as the livestock and poultry industries, produce "growing items," which can be defined as items whose weight and value constantly increase over time. This paper details the design and optimization of a sustainable inventory model for such items that considers the environmental effects of Greenhouse Gases (GHGs) emitted from produced manure, fermentation processes, and transportation, as well as their cost with respect to carbon tax. Furthermore, the proposed model also considers the growth patterns for all dead and live grown items, along with mortality and survival probabilities. Additionally, the sustainability of the period length and order quantity of items is optimized in order to minimize the cost function, while still satisfying the environmental constraints on the main sources of emissions. Through a series of examples, we show that our sustainable Economic Order Quantity (EOQ) model is suitable for applications dealing with growing items. To solve these examples, we utilized the "hho-GWO" algorithm, which is a novel hybrid metaheuristic that combines the advantages of the Harris Hawks Optimizer (hho) and Grey Wolf Optimization (GWO) algorithms to find much better solutions in fewer iterations. The performance of the hho-GWO algorithm is compared to that of the hho and GWO algorithms, as well as Interior Point (IP) solver, with respect to different measures such as quality of generated solutions and CPU-Time. The results indicated that, compared to the hho and GWO algorithms on their own, the hho-GWO algorithm generates much better solutions for the proposed model, while also minimizing CPU-Time. The results obtained for the model showed that considering sustainability factors for growing items not only increases the order quantity, but it also shortens the growth cycle significantly. Finally, sensitivity analyses of the parameters and decision variables identified some practical approaches that can be utilized by inventory managers in

关键词： Sustainability Growing items Inventory management Agri-business hho algorithm GWO algorithm

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