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Optimising SMIB system stability: FOPID controller tuning via harris hawks optimisation

EVOLUTIONARY INTELLIGENCE 2025年第1期18卷 1-19页

作者： Kirange, Yogesh Kalidas Nema, Pragya Oriental Univ Dept Elect & Elect Engn Indore Madhya Pradesh India

Current power system stabilizer designs, including fuzzy-PID and Lead-Lag compensators, need help with adaptability and complexity in diverse and evolving power system environments. Conventional tuning methods like the Newton-Raphson approach and optimization strategies like particle swarm optimization, genetic algorithms, and cuckoo search algorithms face challenges in achieving optimal performance for Fractional Order Proportional Integral Derivative (FOPID) controllers. There is a critical need for innovative tuning methods that offer enhanced adaptability and performance in complex power system stability analysis. This research contributes to the advancement of power system stability analysis, specifically in SMIB systems, offering insights into optimizing FOPID controllers utilizing the innovative harris hawks optimization (HHO) algorithm. The work expects these findings to broaden the implications for power system control and enhance the stability of Single Machine Infinite Bus (SMIB) systems, thus fostering the resilience and reliability of modern electrical infrastructure. The FOPID controller encompasses fractional order parameters, encompassing proportional, integral, and derivative gain, integral order, and derivative order, each exerting a substantial influence on control responses and stability. This research harnesses HHO, an optimization technique inspired by nature, to fine-tune FOPID parameters. The investigation involves initializing the SMIB model, formulating an objective function to minimize control errors, and employing HHO iteratively to refine the FOPID controller. The outcomes reveal enhanced stability, diminished overshoot, accelerated settling time, and transient response.

关键词： Meta-heuristic algorithm harris hawk optimization algorithm Fractional order control Power system stability FOPID controller tuning parameters

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CM-UTC: A Cost-sensitive Matrix based Method for Unknown Encrypted Traffic Classification

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COMPUTER JOURNAL 2024年第7期67卷 2441-2452页

作者： Gao, Zhiyuan Li, Jinguo Wang, Liangliang He, Yin Yuan, Peichun Shanghai Univ Elect Power Coll Comp Sci & Technol Shanghai 201306 Peoples R China

Deep learning has been widely adopted in the field of network traffic classification due to its unique advantages in handling encrypted network traffic. However, most existing deep learning models can only classify known encrypted traffic that has been sampled and labeled. In this paper, we propose CM-UTC, a cost-sensitive matrix-based method for classifying unknown encrypted traffic. CM-UTC explores the probability distribution of the DNN output layer to filter out the unknown classes and further designs a cost-sensitive matrix to address the class imbalance problem. Additionally, we propose the utilization of the harris hawk optimization algorithm to modify the model parameters and improve its performance. The experiments are validated on two different datasets, and the results demonstrate that CM-UTC not only outperforms existing methods in terms of overall performance but also exhibits superior capability in correctly identifying samples from the minority class.

关键词： cost-sensitive learning harris hawk optimization algorithm encrypted traffic classification

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An Improved harris hawks optimization algorithm with Multi-strategy for Community Detection in Social Network

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Journal of Bionic Engineering 2023年第3期20卷 1175-1197页

作者： Farhad Soleimanian Gharehchopogh Department of Computer Engineering Urmia BranchIslamic Azad UniversityUrmia969Iran

The purpose of community detection in complex networks is to identify the structural location of nodes. Complex network methods are usually graphical, with graph nodes representing objects and edges representing connections between things. Communities are node clusters with many internal links but minimal intergroup connections. Although community detection has attracted much attention in social media research, most face functional weaknesses because the structure of society is unclear or the characteristics of nodes in society are not the same. Also, many existing algorithms have complex and costly calculations. This paper proposes different harris hawk optimization (HHO) algorithm methods (such as Improved HHO Opposition-Based Learning(OBL) (IHHOOBL), Improved HHO Lévy Flight (IHHOLF), and Improved HHO Chaotic Map (IHHOCM)) were designed to balance exploitation and exploration in this algorithm for community detection in the social network. The proposed methods are evaluated on 12 different datasets based on NMI and modularity criteria. The findings reveal that the IHHOOBL method has better detection accuracy than IHHOLF and IHHOCM. Also, to offer the efficiency of the , state-of-the-art algorithms have been used as comparisons. The improvement percentage of IHHOOBL compared to the state-of-the-art algorithm is about 7.18%.

关键词： Bionic algorithm Complex network Community detection harris hawk optimization algorithm Opposition-based learning Levy flight Chaotic maps

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harris hawk Optimized Interactive Multi-model Target Tracking Method Using Particle Filtering 6th

Harris Hawk Optimized Interactive Multi-model Target Trackin...

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6th Conference on Intelligent Networked Things (CINT)

作者： Wei, Wei Li, Chen Beihang Univ Sch Mech Engn & Automat Beijing 100083 Peoples R China Beihang Univ Jiangxi Res Inst Nanchang 330096 Jiangxi Peoples R China Nanchang Hangkong Univ Sch Aeronaut Mfg Engn Nanchang 330063 Jiangxi Peoples R China

ISBN: (纸本)9789819739479;9789819739486

This paper proposes a harris hawk-optimized particle filtering algorithm integrated with interactive multiple models for dynamic target tracking. The algorithm simulates the hunting behavior of individual harris hawks to address particle impoverishment in traditional resampling processes. Additionally, it improves the hunting mechanism using strategies from the wolf pack algorithm, particularly enhancing global search. Furthermore, an interactive multiple model algorithm based on three motion models is designed and integrated. Simulation results demonstrate that the proposed algorithm outperforms existing methods in terms of accuracy and stability under varying noise intensities.

关键词： Particle filtering harris hawk optimization algorithm interactive multiple models particle impoverishment

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Ship Attitude Prediction Model Based on Cross-Parallel algorithm Optimized Neural Network

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IEEE ACCESS 2022年 10卷 77857-77871页

作者： Jiang, Yanshu Jia, Mingqi Zhang, Biao Deng, Liwei Harbin Univ Sci & Technol Sch Automat Heilongjiang Prov Key Lab Complex Intelligent Sys Harbin 150080 Peoples R China

In recent years, with the development of modern technology and industrialization, the shipbuilding industry has become an essential field in a country. Offshore operations, such as helicopter take-off and landing, and ship-to-ship cargo replenishment, need an accurate prediction of ship motion attitude, not only to improve efficiency but also to protect crew members' lives. However, using the single model has limited influence on prediction. Therefore, this paper proposed a Cross-Parallel optimization algorithm framework, which made full use of the development ability of the harris hawk optimization algorithm (HHO) and the searching ability of the Sine-Cosine algorithm (SCA). In this paper, we use Rosenbrock and Rastrigin functions for testing. The results show that the Cross-Parallel optimization algorithm has faster search speed and stronger optimization capability. The optimization algorithm is used to optimize the hyperparameters of Long Short-Term Memory (LSTM) neural networks. It improves the network's training ability to predict the ship's attitude. The proposed method in this paper is compared with 9 models such as BP, LSTM, GRU, BILSTM and TCN. It is tested with three degrees of freedom under different sea states. MAE and RMSE are used as the evaluation indexes of this paper. In the six sets of experimental results, compared with the other nine models, the method in this paper can reduce the MAE by up to 9.2%, 7.2%, 4.7%, 5.4%, 6.4%, and 6.8%, respectively. When using RMSE as the evaluation index, the decrease was at most 10.1%, 8.7%, 5.6%, 7.1%, 7.5%, and 8.2%. The experimental results show that the method in this paper has the highest prediction accuracy.

关键词： Prediction algorithms optimization Marine vehicles Machine learning algorithms Mathematical models Biological neural networks Predictive models harris hawk optimization algorithm LSTM sine cosine optimization algorithm ship attitude prediction SCA-HHO-LSTM

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An energy-aware virtual machine placement method in cloud data centers based on improved harris hawks optimization algorithm

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Computing 2025年第6期107卷 1-40页

作者： Mehrabadi, Zahra Karimi Fartash, Mehdi Torkestani, Javad Akbari Department of Computer Engineering Arak Branch Islamic Azad University Arak Iran

The escalating adoption of cloud computing has presented a significant environmental and economic challenge because of the corresponding surge in energy consumption within cloud data centers. Virtualization technology is widely recognized as a popular method for mitigating energy consumption in data centers. Providing an optimal and method for virtual machine placement (VMP) contributes crucially to server integration. This article introduces a novel VMP algorithm that combines the harris hawk and Genetics optimization algorithms, along with resource-aware allocation. This algorithm primarily minimizes the energy consumed by cloud data centers. Despite heuristic and meta-heuristic algorithms, including IGA POP, the energy-aware VMP strategy proposed in this study aims to minimize energy consumption by optimizing the allocation of physical machines (PMs). The introduced strategy achieves a balanced utilization of resources, comprising CPU, RAM, and bandwidth, decreasing the number of active servers required. The proposed VMP algorithm yields efficiency scores of 2.95 for datasets comprising 50 and 300 virtual machines, respectively. The data show a decrease in energy consumption by 1.83%, as well as reductions of 3.84 and 2.17% in the number of active PMs.

关键词： Cloud computing Energy-aware Virtual machine placement harris hawk optimization algorithm

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Inversion study of soil organic matter content based on reflectance spectroscopy and the improved hybrid extreme learning machine

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INFRARED PHYSICS & TECHNOLOGY 2023年 128卷

作者： Xiao, Dong Huang, Jie Li, Jian Fu, Yanhua Mao, Yachun Li, Zhenni Bao, Nisha Northeastern Univ Coll Informat Sci & Engn Shenyang 110819 Peoples R China Tech Serv Polar Unit Chinese Peoples Liberat Army 31453 Shenyang 110000 Peoples R China Northeastern Univ Sch JangHo Architecture Shenyang 110169 Peoples R China Northeastern Univ Sch Resources & Civil Engn Shenyang 110819 Peoples R China

The continuous exploitation of mining resources and a number of other factors have led to serious soil pollution problems. Soil organic matter (SOM) can be used as an important measure of soil fertility and ecological envi-ronment quality in mining areas. The traditional method of measuring SOM content by chemical methods is costly and time-consuming, so it is important to develop a rapid, accurate and economical method to measure SOM content in reclaimed mines for the ecological restoration and the evaluation of the ecological reclamation effect of mines. In this paper, the visible-near infrared spectral data of soil were obtained using an open pit mine as the study area. The three pre-processing methods of multiple scattering correction, continuous removal and Savitzky-Golay smoothing were compared. Then the two-band ratio spectral index RI was constructed to obtain the sensitive band combinations. To address the problem that the HHO-TELM hybrid model tended to converge prematurely, the GHO-TELM model was proposed in this paper. GHO-TELM introduced a gene learning strategy in HHO-TELM. The crossover, mutation and selection mechanisms were used to cultivate high-quality examples, enhance the quality of individuals and prevent premature convergence of the model. GHO-TELM was compared with several machine learning models. The experimental results showed that GHO-TELM obtained the best performance indexes, with correlation coefficient (r) of 0.9586, and relative percentage deviation (RPD) of 3.0823, indicating that the model had a good prediction effect.

关键词： Visible -near infrared spectrum Soil organic matter Extreme learning machine harris hawk optimization algorithm Gene learning strategy

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A Novel Brownian Motion-based Hybrid Whale optimization algorithm

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JOURNAL OF INTERNET TECHNOLOGY 2023年第3期24卷 795-808页

作者： Zhang, Liyi Wang, Xiaolin Liu, Ting Zhang, Yong Hu, Yongsheng Tianjin Univ Commerce Informat Engn Coll Tianjin Peoples R China Binzhou Univ Sch Informat Engn Binzhou Peoples R China

The Whale optimization algorithm (WOA) has the characteristics of simple implementation and few adjustment parameters, which is remarkable in the optimization algorithm. However, there are shortcomings such as premature convergence, slow convergence in the later period, and low search accuracy. For these shortcomings, a novel Brownian motion-based hybrid whale optimization algorithm (HWOA) is proposed. The search strategy in the harris hawk optimization algorithm (HHO) is adopted to improve the global search ability of the algorithm, and a soft besiege with progressive rapid dives is introduced to solve the problems of premature convergence and slow convergence. Besides, the Brownian motion model is used to replace WOA. The random parameters in the distance formula are calculated to better simulate the prey's escape during the predation process, and help to jump out of the local optimum. The simulation of 23 benchmark functions shows that compared with the classic and HWOA and metaheuristic, the convergence accuracy and speed have been improved, and the local optimum can be effectively jumped out. At the same time, 10 CEC06-2019 test functions are used to test and analyze it. Compared with WOA, HWOA has better search results, which verifies the superiority of the improved algorithm.

关键词： Whale optimization algorithm harris hawk optimization algorithm Brownian motion Metaheuristic CEC06-2019

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Design Method for Travel E-commerce Platform Based on HHO improved K-means Clustering algorithm

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EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS 2024年第5期11卷

作者： Dang, Mihua Yang, Suiming Xian Traff Engn Inst Sch Humanities & Management Xian 710300 Shaanxi Peoples R China

Convenient and intelligent tourism product recommendation method, as the key technology of tourism E-commerce platform design, not only provides academic value to the research of tourism E-commerce platform, but also improves the efficiency of personalized recommendation of tourism products. In order to improve the quality of tourism recommendation, this paper proposes a tourism E-commerce platform design method based on HHO improved K-means clustering algorithm. Firstly, the harris optimization algorithm is used to improve the K-means algorithm to construct a user-oriented tourism product recommendation strategy;then, combined with the XGBoost algorithm, an item-oriented tourism product recommendation strategy is proposed;secondly, the two strategies are mixed to construct a personalized tourism product recommendation model. Finally, the effectiveness of the proposed method is verified by simulation experiment analysis. The results show that the recommendation accuracy of the tourism E-commerce platform design method proposed in this paper reaches more than 90%, and the recommendation response time meets the real-time requirements, which can provide personalized tourism product recommendation for platform users and enhance the purchase of tourism products.

关键词： travel E -commerce platform design K -means clustering algorithm harris hawk optimization algorithm XGBoost

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Titul Research on Wireless Network Coverage for Transformation and Upgrading of Exhibition Management with Artificial Intelligence Technology

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APPLIED MATHEMATICS AND NONLINEAR SCIENCES 2023年

作者： Dou, Peilin Zheng, Guohua Shanghai Int Studies Univ Xianda Coll Econ & Humanities Shanghai 200080 Peoples R China Shanghai Xunmeng Informat Technol Co Ltd Shanghai 200050 Peoples R China

The rapid development of artificial intelligence and modern communication technology provides a powerful opportunity to realize the transformation, upgrading and high-quality development of exhibition management. To solve the optimal coverage problem of wireless network in the exhibition area, a new generation of artificial intelligence algorithm is introduced to propose a novel method for solving the network coverage problem based on harris hawk optimization (HHO) algorithm. From the perspective of maximizing coverage and minimizing coverage redundancy, the multi-objective coverage optimization problem of wireless network nodes is optimally extracted into a function and solved using the improved HHO (IHHO) algorithm. Through the simulation of the wireless network coverage in the exhibition area, the experimental results show that the comprehensive evaluation index score of the optimization algorithm reaches 98.03%, which is higher than the traditional particle swarm optimizer (PSO) and the standard HHO. The proposed IHHO algorithm can be effectively applied to the network coverage problem and has a greater advantage over the traditional optimization methods in terms of improving node coverage, and has great practicality.

关键词： Keyword Artificial intelligence exhibition management wireless network coverage optimization harris hawk optimization algorithm

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