检索结果-内蒙古大学图书馆

harris hawks optimization algorithm based on elite fractional mutation for data clustering

APPLIED INTELLIGENCE 2022年第10期52卷 11407-11433页

作者： Guo, Wenyan Xu, Peng Dai, Fang Hou, Zhuolin Xian Univ Technol Sch Sci Xian 710054 Shaanxi Peoples R China

The density peak clustering (DPC) algorithm is an efficient clustering algorithm that can automatically find the class center and realize arbitrary shape data clustering. The design of the local density is the core of the DPC algorithm, and the value of the cutoff distance parameter involved in the calculation of the local density has a great impact on the performance of the algorithm. In this paper, based on analyzing the defects of the local density design of the DPC algorithm, we use the cosine similarity and exponential decay function to establish a new method of segmented local density calculation, and build an optimization model for the selection of cutoff distance parameters. A new density peak clustering algorithm (NDPC) is proposed. When solving the model, an improved harris hawks optimization algorithm (FHHO)based on elite fractional derivative mutation is proposed. Simultaneously, the FHHO-NDPC algorithm combining FHHO and NDPC algorithm is put forward. The FHHO algorithm uses Grunwald-Letnikov (G-L) fractional derivative to correct the elite population which changes with the number of iterations and uses a more random exploration strategy to enhance the exploration performance of HHO algorithm. Therefore, the proposed FHHO algorithm inherits the merits of fractional derivative memory, mends the ability of exploration and exploitation by random exploration strategy, and refrains from the algorithm sinking into local optimum. Two groups of experiments are devised simultaneously to verify the significance and usefulness of the FHHO algorithm and FHHO-NDPC algorithm. Experimental results on the CEC2017 test set show that FHHO has obvious dominant positions in solving high dimensional problems in terms of convergence speed and solution precision compared with other representative intelligent algorithms. The clustering results of twelve representative data sets show that FHHO-NDPC has an excellent clustering performance, which provides a useful reference for t

关键词： Density peak clustering harris hawks optimization algorithm Grunwald- Letnikov fractional derivative Elite individuals Data clustering

来源：评论

学校读者我要写书评

暂无评论

harris hawks optimization algorithm and BP neural network for ultra- wideband indoor positioning

引用

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第9期19卷 9098-9124页

作者： Chen, Xiaohao Fu, Maosheng Liu, Zhengyu Jia, Chaochuan Liu, Yu West Anhui Univ Coll Elect & Informat Engn Luan Peoples R China Shandong Univ Sci & Technol Robot Res Ctr Qingdao Peoples R China

Traditional back propagation neural networks (BPNNs) for ultrawideband (UWB) indoor localization can effectively improve localization accuracy, although there is high likelihood of becoming trapped in nearby minima. To solve this problem, the random weights and thresholds of the BPNN are optimized using the harris hawks optimization algorithm (HHO) to obtain the optimal global solution to enhance the UWB indoor positioning accuracy and NLOS resistance. The results show that the predicted trajectory of the HHO and BPNN hybrid algorithm (HHO-BP) matches the actual position in the two-dimensional localization scenario with four base stations;the optimized average positioning error is effectively reduced in both indoor LOS and NLOS environments. In the LOS environment, the total mean error of the traditional BPNN algorithm is 6.52 cm, which is 26.99% better than the UWB measurement error;in the NLOS environment, the total mean error of the conventional BPNN is 14.82 cm, which is 50.08% better than the UWB measurement error. The HHO??? BP algorithm is further optimized on this basis, and the total mean error in the LOS environment is 4.50 cm, which is 22.57% better than the conventional BPNN algorithm;in the NLOS environment, the total mean error is 9.56 cm, which is 17.54% better than the conventional BPNN algorithm. The experimental findings suggest that the approach has greater calibration accuracy and stability than BPNN, making it a viable choice for scenarios requiring high positional precision.

关键词： UWB BPNN harris hawks optimization algorithm line-of-sight non-line-of-sight indoor positioning

来源：评论

学校读者我要写书评

暂无评论

RESEARCH ON PARTICLE SIZE AND CONSUMPTION COLLABORATIVE optimization PROBLEMS OF GRINDING PROCESS BASED ON AN ENHANCED harris hawks optimization algorithm

引用

JOURNAL OF INDUSTRIAL AND MANAGEMENT optimization 2024年第8期20卷 2719-2739页

作者： Liu, Yilin Dai, Jiayang Chen, Xingyu Luo, Yasong Guangxi Univ Sch Elect Engn Guangxi Key Lab Intelligent Control & Maintenance Nanning 530000 Peoples R China

The grinding process is an essential part of iron and steel smelting. Due to the changeable ore source and the complex dynamic characteristics of the process, the grinding particle size often cannot meet the requirements, resulting in an increase in process energy and material consumption, as well as reworking. In response to the above problems, the following goal was achieved through this study: a collaborative optimization problem for minimization of the grinding particle size error, process water, amountand electricity consumption has been established, which yields the grinding particle size in real time through the use of a neural network. In order to improve the optimization performance, an enhanced harris hawks optimization (EHHO) algorithm has been developed. EHHO strengthens the team cooperation between individuals in the algorithm initialization phase and location update phase, as well as introduces an attenuation factor in the search phase to improve the stability and accuracy of the algorithm. The effectiveness of the proposed method is demonstrated through the use of some benchmark functions and an industrial grinding process. The results show that the enhanced harris hawks optimization algorithm has better optimization accuracy and generalizability;furthermore, it can be effectively applied to the collaborative optimization of grinding process.

关键词： Collaborative optimization harris hawks optimization algorithm grinding particle size grinding process

来源：评论

学校读者我要写书评

暂无评论

An Optimal Robust State Feedback Controller for the AVR System-Based harris hawks optimization algorithm

引用

ELECTRIC POWER COMPONENTS AND SYSTEMS 2021年第16-17期48卷 1684-1694页

作者： Mary, Ali Hussien Miry, Abbas H. Miry, Mohammed H. Univ Baghdad Al Khwarizmi Coll Engn Mechatron Engn Dept Baghdad 10070 Iraq Mustansiriyah Univ Elect Engn Dept Baghdad Iraq Univ Technol Baghdad Commun Engn Dept Baghdad Iraq

This paper presents a robust tracking controller for the automatic voltage regulator (AVR) system, where both system uncertainties and external disturbances are taken into account. The proposed control scheme combines optimal feedback control with robust control. The parameter gains of the proposed controller have been selected based on Lyapunov theorem to ensure the stability of the closed-loop control system. Moreover, the parameters of the feedback control term are optimized using the harris hawks optimization (HHO) algorithm. The performance of the proposed controller is compared with several Proportional Integral Derivative (PID) controllers optimized with different optimization algorithms. In addition, the fractional order PID (FOPID) controller is used for comparison. The validity of the proposed control scheme is illustrated through the simulation of the AVR system with three cases of simulation have been discussed.

关键词： robust control optimal control AVR harris hawks optimization algorithm state feedback control

来源：评论

学校读者我要写书评

暂无评论

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

引用

COMPUTING 2024年第2期106卷 327-351页

作者： Fan, Chencheng Wang, Zhixiao Zhang, Jian Zhao, Jiayu Rui, Xiaobin China Univ Min & Technol Dept Comp Sci Xuzhou 221116 Jiangsu Peoples R China Minist Educ Mine Digitizat Engn Res Ctr Xuzhou 221116 Jiangsu Peoples R China

Influence Maximization (IM) is an important topic in the field of social network analysis, and is widely used in viral marketing, recommendation systems, rumor prevention and other fields. The meta-heuristic method has excellent performance because of high scalability and low complexity, however, the objective function of existing meta-heuristic methods can only be applied to propagation models with small probabilities. In addition, there is room for further improvement in performance of meta-heuristic methods. In order to solve the above problems, this paper transforms the influence maximization problem into an optimization problem and designs a novel objective function based on the six degrees of separation theory of social networks. Then, with the designed objective function, this paper discretizes the harris hawks optimization (HHO) algorithm by redefining the energy and location representation rules for influence maximization problem. Experimental results on eight real datasets demonstrate that the proposed objective function exhibits high accuracy and generality, suitable for various probability propagation models. With the exploration and exploitation process in steps, dynamically using different strategies various situations, the proposed DHHO algorithm exhibits better performance in dealing with influence maximization problem, outperforming the state-of-the-art methods.

关键词： Influence maximization Meta-heuristic optimization algorithm Six degrees of separation theory harris hawks optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A mixed harris hawks optimization algorithm based on the pinhole imaging strategy for solving numerical optimization problems

引用

JOURNAL OF SUPERCOMPUTING 2023年第14期79卷 15270-15323页

作者： Zeng, Liang Li, Yanyan Zhang, Hao Li, Ming Wang, Shanshan Hubei Univ Technol Sch Elect & Elect Engn Wuhan 430068 Hubei Peoples R China Hubei Univ Technol Hubei Key Lab High Efficiency Utilizat Solar Energ Wuhan 430068 Hubei Peoples R China Hubei Univ Technol Xiangyang Ind Inst Xiangyang 441100 Hubei Peoples R China

The harris hawks optimization (HHO) algorithm is a new metaheuristic algorithm proposed in recent years. Due to the shortcomings of this algorithm in solving complex high-dimensional optimization problems with a slow convergence speed, low accuracy, and the high likelihood to fall into local optimum, a mixed harris hawks optimization (MHHO) algorithm based on the pinhole imaging strategy is proposed, including four strategies to improve the optimization performance. Firstly, the pinhole imaging strategy is used to enable the harris' hawks to approach the optimal solution faster and accelerate convergence. Secondly, the spiral parameter is introduced into the exploration phase to help the searching paths of the harris' hawks more diverse and improve the global search ability of the algorithm. Finally, the greedy strategy of the aquila optimization algorithm and the position update strategy of the flower pollination optimization algorithm are embedded in the exploitation stage to make the algorithm jump out of local optimum effectively. To verify the effectiveness of the proposed MHHO algorithm, it is compared with the classical HHO algorithm and 16 other state-of-the-art algorithms, and extensively tested on 23 well-known benchmark functions, the IEEE CEC2017 test sets, and three complex constrained engineering optimization problems. The test results show that MHHO achieves the top ranking on both the benchmark functions and the CEC2017 test sets, demonstrating its superior performance in terms of faster convergence speed and higher accuracy.

关键词： harris hawks optimization algorithm Pinhole imaging Aquila optimization algorithm Flower pollination optimization

来源：评论

学校读者我要写书评

暂无评论

Improved harris hawks optimization algorithm based on quantum correction and Nelder-Mead simplex method

引用

MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第8期19卷 7606-7648页

作者： Zhu, Cheng Zhang, Yong Pan, Xuhua Chen, Qi Fu, Qingyu Tianjin Univ Commerce Sch Informat Engn Tianjin 300134 Peoples R China

harris hawks optimization (HHO) algorithm is a kind of intelligent algorithm that simulates the predation behavior of hawks. It suffers several shortcomings, such as low calculation accuracy, easy to fall into local optima and difficult to balance exploration and exploitation. In view of the above problems, this paper proposes an improved HHO algorithm named as QC-HHO. Firstly, the initial population is generated by H??non Chaotic Map to enhance the randomness and ergodicity. Secondly, the quantum correction mechanism is introduced in the local search phase to improve optimization accuracy and population diversity. Thirdly, the Nelder-Mead simplex method is used to improve the search performance and breadth. Fourthly, group communication factors describing the relationship between individuals is taken into consideration. Finally, the energy consumption law is integrated into the renewal process of escape energy factor E and jump distance J to balance exploration and exploitation. The QC-HHO is tested on 10 classical benchmark functions and 30 CEC2014 benchmark functions. The results show that it is superior to original HHO algorithm and other improved HHO algorithms. At the same time, the improved algorithm studied in this paper is applied to gas leakage source localization by wireless sensor networks. The experimental results indicate that the accuracy of position and gas release rate are excellent, which verifies the feasibility for application of QC-HHO in practice.

关键词： map quantum correction Nelder-Mead simplex group communication factor swarm intelligence optimization harris hawks optimization algorithm H?non chaotic map

来源：评论

学校读者我要写书评

暂无评论

An Adaboost Support Vector Machine Based harris hawks optimization algorithm for Intelligent Quotient Estimation from MRI Images

引用

NEURAL PROCESSING LETTERS 2023年第1期55卷 519-536页

作者： Thilakavathy, P. Diwan, B. Anna Univ Dept Informat & Commun Engn Chennai Tamil Nadu India St Josephs Coll Engn Dept Comp Sci & Engn Chennai Tamil Nadu India

Human intelligence is measured using the Intelligent Quotient (IQ) score which is derived from a range of tests. Due to a lack of a large dataset, IQ was estimated utilizing consistent scanning approaches. Multiple datasets scanned from various locations are integrated using different scanning factors and procedures. As a result, there was a lot of diversity among the datasets. To overcome the aforementioned problems, this work presented a novel technique for IQ assessment based on data from Magnetic Resonance Imaging dataset. The pre-processing step focuses on regions like cerebrospinal fluid, white matter (WM), tissue segmentation, cerebellum removal, and skull stripping. Based on Gray Level Co-occurrence Matrix features, we have extracted energy, entropy, contrast, homogeneity, correlation, shade, and prominence features. The Principal Component Analysis based Discriminant Analysis with Linear Discriminant Analysis (PCA-based DP-LDA) selects the optimal feature selection score values. The Adaboost Support Vector Machine (Adaboost SVM)-based harris hawks optimization (HHO) technique is used to estimate IQ values. The HHO algorithm is used to optimize the number of iterations, error threshold, and weighting factors in AdaBoost SVM, resulting in the estimation of optimal IQ values. Because memory and cognitive abilities are commonly tested in IQ testing, changes in Grey Matter/White Matter (GM/WM) tissues in these ROIs may have an influence on measuring human intelligence. Experimental results show a substantial connection between cortical thickness, laminary difference, and hierarchical position estimation for visual and auditory hierarchies and somatosensory hierarchies. The proposed Adaboost-SVM with the HHO method offers the lowest Root Mean square error value of 8.521 when compared with the existing techniques and gives a classification accuracy of 98% when evaluated with tenfold cross-validation.

关键词： Human intelligence IQ estimation Adaboost SVM harris hawks optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

A robot path planning method using improved harris hawks optimization algorithm

引用

MEASUREMENT & CONTROL 2024年第4期57卷 469-482页

作者： Li, Changyong Si, Qing Zhao, Jianan Qin, Pengbo Xinjiang Univ Urumqi Xinjiang Peoples R China

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 Levy 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.

关键词： Chebyshev chaos mapping harris hawks optimization algorithm improved energy factor mobile robot path planning

来源：评论

学校读者我要写书评

暂无评论

An Improved Multi-Objective harris hawks optimization algorithm for Solving EDM Problems

引用

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2024年 1-46页

作者： Uddin, Md Piyar Majumder, Arindam Barma, John Deb Mirjalili, Seyedali Natl Inst Technol Agartala Mech Engn Dept Jirania 799046 India Torrens Univ Australia Ctr Artificial Intelligence Res & Optimizat Brisbane Qld 4006 Australia VSB TU Ostrava Fac Elect Engn & Comp Sci Ostrava 70080 Czech Republic

In this study, an improved multi-objective harris hawks optimization algorithm (IMHHO) is developed to solve optimization problems related to the EDM process. In the IMHHO algorithm, an exponentially decreasing strategy is applied to update the escaping energy. The candidate's selection for the exploration phase of the improved algorithm is carried out using a newly proposed crowding distance-based approach. For sorting the non-dominated solutions, the algorithm implemented the fast non-dominated sorting technique adopted from NSGA-II. The proposed algorithm is tested on eleven widely used benchmark problems and ten existing instances of the EDM process. Both unconstrained and constrained benchmark problems are considered for this study. The performance of the algorithm is measured in terms of coverage, spacing, and CPU time. A comparison of IMHHO with existing MHHO and four recently developed population-based optimization techniques is then performed to evaluate the algorithm's capability. The results and findings demonstrate IMHHO as better than MHHO, with an average improvement of 83.94%, 38.06%, and 79.53% in terms of coverage, spacing, and CPU time, respectively. The IMHHO, MMRO, and MSMA results show improved coverage, spacing, and CPU time of IMHHO with an average performance enhancement of 48.68%, 51.30%, and 91.28%, respectively, compared to the other two algorithms. The comparison of IMHHO with MBO and MCSA reveals an overall comparable performance in terms of coverage and average superiority of 56.96% and 98.36% in spacing and CPU time, respectively.

关键词： harris hawks optimization algorithm EDM Pareto optimal set Multi-objective optimization Global optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：