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An Improved Harris Hawks Optimization Algorithm with Multi-strategy for Community Detection in Social Network 认领 引用 被引量：8
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作者 Farhad Soleimanian Gharehchopogh 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第3期1175-1197,共23页
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 conne... 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%. 展开更多
关键词 Bionic algorithm Complex network Community detection Harris hawk optimization algorithm Opposition-based learning Levy flight Chaotic maps
An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem 认领 引用 被引量：4
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作者 Zhaolin Lv Yuexia Zhao +2 位作者 Hongyue Kang Zhenyu Gao Yuhang Qin 《Computers, Materials & Continua》 SCIE EI 2024年第2期2337-2360,共24页
Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been... Flexible job shop scheduling problem(FJSP)is the core decision-making problem of intelligent manufacturing production management.The Harris hawk optimization(HHO)algorithm,as a typical metaheuristic algorithm,has been widely employed to solve scheduling problems.However,HHO suffers from premature convergence when solving NP-hard problems.Therefore,this paper proposes an improved HHO algorithm(GNHHO)to solve the FJSP.GNHHO introduces an elitism strategy,a chaotic mechanism,a nonlinear escaping energy update strategy,and a Gaussian random walk strategy to prevent premature convergence.A flexible job shop scheduling model is constructed,and the static and dynamic FJSP is investigated to minimize the makespan.This paper chooses a two-segment encoding mode based on the job and the machine of the FJSP.To verify the effectiveness of GNHHO,this study tests it in 23 benchmark functions,10 standard job shop scheduling problems(JSPs),and 5 standard FJSPs.Besides,this study collects data from an agricultural company and uses the GNHHO algorithm to optimize the company’s FJSP.The optimized scheduling scheme demonstrates significant improvements in makespan,with an advancement of 28.16%for static scheduling and 35.63%for dynamic scheduling.Moreover,it achieves an average increase of 21.50%in the on-time order delivery rate.The results demonstrate that the performance of the GNHHO algorithm in solving FJSP is superior to some existing algorithms. 展开更多
关键词 Flexible job shop scheduling improved Harris hawk optimization algorithm(GNHHO) premature convergence maximum completion time(makespan)
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Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network 认领 引用 被引量：17
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作者 Bhatawdekar Ramesh Murlidhar Hoang Nguyen +4 位作者 Jamal Rostami XuanNam Bui Danial Jahed Armaghani Prashanth Ragam Edy Tonnizam Mohamad 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2021年第6期1413-1427,共15页
In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead t... In mining or construction projects,for exploitation of hard rock with high strength properties,blasting is frequently applied to breaking or moving them using high explosive energy.However,use of explosives may lead to the flyrock phenomenon.Flyrock can damage structures or nearby equipment in the surrounding areas and inflict harm to humans,especially workers in the working sites.Thus,prediction of flyrock is of high importance.In this investigation,examination and estimation/forecast of flyrock distance induced by blasting through the application of five artificial intelligent algorithms were carried out.One hundred and fifty-two blasting events in three open-pit granite mines in Johor,Malaysia,were monitored to collect field data.The collected data include blasting parameters and rock mass properties.Site-specific weathering index(WI),geological strength index(GSI) and rock quality designation(RQD)are rock mass properties.Multi-layer perceptron(MLP),random forest(RF),support vector machine(SVM),and hybrid models including Harris Hawks optimization-based MLP(known as HHO-MLP) and whale optimization algorithm-based MLP(known as WOA-MLP) were developed.The performance of various models was assessed through various performance indices,including a10-index,coefficient of determination(R2),root mean squared error(RMSE),mean absolute percentage error(MAPE),variance accounted for(VAF),and root squared error(RSE).The a10-index values for MLP,RF,SVM,HHO-MLP and WOA-MLP are 0.953,0.933,0.937,0.991 and 0.972,respectively.R2 of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models. 展开更多
关键词 Flyrock Harris hawks optimization(HHO) Multi-layer perceptron(MLP) Random forest(RF) Support vector machine(SVM) Whale optimization algorithm(WOA)
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An Improved Harris Hawk Optimization Algorithm 认领 引用
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作者 GuangYa Chong Yongliang YUAN 《Mechanical Engineering Science》 2024年第1期21-25,共5页
Aiming at the problems that the original Harris Hawk optimization algorithm is easy to fall into local optimum and slow in finding the optimum,this paper proposes an improved Harris Hawk optimization algorithm(GHHO).F... Aiming at the problems that the original Harris Hawk optimization algorithm is easy to fall into local optimum and slow in finding the optimum,this paper proposes an improved Harris Hawk optimization algorithm(GHHO).Firstly,we used a Gaussian chaotic mapping strategy to initialize the positions of individuals in the population,which enriches the initial individual species characteristics.Secondly,by optimizing the energy parameter and introducing the cosine strategy,the algorithm's ability to jump out of the local optimum is enhanced,which improves the performance of the algorithm.Finally,comparison experiments with other intelligent algorithms were conducted on 13 classical test function sets.The results show that GHHO has better performance in all aspects compared to other optimization algorithms.The improved algorithm is more suitable for generalization to real optimization problems. 展开更多
关键词 Harris Hawk optimization algorithm chaotic mapping cosine strategy function optimization
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Computing Connected Resolvability of Graphs Using Binary Enhanced Harris Hawks Optimization 认领 引用 被引量：1
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作者 Basma Mohamed Linda Mohaisen Mohamed Amin 《Intelligent Automation & Soft Computing》 SCIE 2023年第5期2349-2361,共13页
In this paper,we consider the NP-hard problem offinding the minimum connected resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distanc... In this paper,we consider the NP-hard problem offinding the minimum connected resolving set of graphs.A vertex set B of a connected graph G resolves G if every vertex of G is uniquely identified by its vector of distances to the ver-tices in B.A resolving set B of G is connected if the subgraph B induced by B is a nontrivial connected subgraph of G.The cardinality of the minimal resolving set is the metric dimension of G and the cardinality of minimum connected resolving set is the connected metric dimension of G.The problem is solved heuristically by a binary version of an enhanced Harris Hawk Optimization(BEHHO)algorithm.This is thefirst attempt to determine the connected resolving set heuristically.BEHHO combines classical HHO with opposition-based learning,chaotic local search and is equipped with an S-shaped transfer function to convert the contin-uous variable into a binary one.The hawks of BEHHO are binary encoded and are used to represent which one of the vertices of a graph belongs to the connected resolving set.The feasibility is enforced by repairing hawks such that an addi-tional node selected from V\B is added to B up to obtain the connected resolving set.The proposed BEHHO algorithm is compared to binary Harris Hawk Optimi-zation(BHHO),binary opposition-based learning Harris Hawk Optimization(BOHHO),binary chaotic local search Harris Hawk Optimization(BCHHO)algorithms.Computational results confirm the superiority of the BEHHO for determining connected metric dimension. 展开更多
关键词 Connected resolving set binary optimization harris hawks algorithm
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An Improved Jump Spider Optimization for Network Traffic Identification Feature Selection 认领 引用 被引量：2
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作者 Hui Xu Yalin Hu +1 位作者 Weidong Cao Longjie Han 《Computers, Materials & Continua》 SCIE EI 2023年第9期3239-3255,共17页
The massive influx of traffic on the Internet has made the composition of web traffic increasingly complex.Traditional port-based or protocol-based network traffic identification methods are no longer suitable for to... The massive influx of traffic on the Internet has made the composition of web traffic increasingly complex.Traditional port-based or protocol-based network traffic identification methods are no longer suitable for today’s complex and changing networks.Recently,machine learning has beenwidely applied to network traffic recognition.Still,high-dimensional features and redundant data in network traffic can lead to slow convergence problems and low identification accuracy of network traffic recognition algorithms.Taking advantage of the faster optimizationseeking capability of the jumping spider optimization algorithm(JSOA),this paper proposes a jumping spider optimization algorithmthat incorporates the harris hawk optimization(HHO)and small hole imaging(HHJSOA).We use it in network traffic identification feature selection.First,the method incorporates the HHO escape energy factor and the hard siege strategy to forma newsearch strategy for HHJSOA.This location update strategy enhances the search range of the optimal solution of HHJSOA.We use small hole imaging to update the inferior individual.Next,the feature selection problem is coded to propose a jumping spiders individual coding scheme.Multiple iterations of the HHJSOA algorithmfind the optimal individual used as the selected feature for KNN classification.Finally,we validate the classification accuracy and performance of the HHJSOA algorithm using the UNSW-NB15 dataset and KDD99 dataset.Experimental results show that compared with other algorithms for the UNSW-NB15 dataset,the improvement is at least 0.0705,0.00147,and 1 on the accuracy,fitness value,and the number of features.In addition,compared with other feature selectionmethods for the same datasets,the proposed algorithmhas faster convergence,better merit-seeking,and robustness.Therefore,HHJSOAcan improve the classification accuracy and solve the problem that the network traffic recognition algorithm needs to be faster to converge and easily fall into local optimum due to high-dimensional features. 展开更多
关键词 Network traffic identification feature selection jumping spider optimization algorithm harris hawk optimization small hole imaging
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Optimization of Resource Allocation in Unmanned Aerial Vehicles Based on Swarm Intelligence Algorithms 认领 引用
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作者 Siling Feng Yinjie Chen +1 位作者 Mengxing Huang Feng Shu 《Computers, Materials & Continua》 SCIE EI 2023年第5期4341-4355,共15页
Due to their adaptability,Unmanned Aerial Vehicles(UAVs)play an essential role in the Internet of Things(IoT).Using wireless power transfer(WPT)techniques,an UAV can be supplied with energy while in flight,thereby ext... Due to their adaptability,Unmanned Aerial Vehicles(UAVs)play an essential role in the Internet of Things(IoT).Using wireless power transfer(WPT)techniques,an UAV can be supplied with energy while in flight,thereby extending the lifetime of this energy-constrained device.This paper investigates the optimization of resource allocation in light of the fact that power transfer and data transmission cannot be performed simultaneously.In this paper,we propose an optimization strategy for the resource allocation of UAVs in sensor communication networks.It is a practical solution to the problem of marine sensor networks that are located far from shore and have limited power.A corresponding system model is summarized based on the scenario and existing theoretical works.The minimum throughputmaximizing object is then formulated as an optimization problem.As swarm intelligence algorithms are utilized effectively in numerous fields,this paper chose to solve the formed optimization problem using the Harris Hawks Optimization and Whale Optimization Algorithms.This paper introduces a method for translating multi-decisions into a row vector in order to adapt swarm intelligence algorithms to the problem,as joint time and energy optimization have two sets of variables.The proposed method performs well in terms of stability and duration.Finally,performance is evaluated through numerical experiments.Simulation results demonstrate that the proposed method performs admirably in the given scenario. 展开更多
关键词 Resource allocation unmanned aerial vehicles harris hawks optimization whale optimization algorithm
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Crisscross Harris Hawks Optimizer for Global Tasks and Feature Selection 认领 引用 被引量：2
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作者 Xin Wang Xiaogang Dong +1 位作者 Yanan Zhang Huiling Chen 《Journal of Bionic Engineering》 SCIE EI CSCD 2023年第3期1153-1174,共22页
Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it under... Harris Hawks Optimizer (HHO) is a recent well-established optimizer based on the hunting characteristics of Harris hawks, which shows excellent efficiency in solving a variety of optimization issues. However, it undergoes weak global search capability because of the levy distribution in its optimization process. In this paper, a variant of HHO is proposed using Crisscross Optimization Algorithm (CSO) to compensate for the shortcomings of original HHO. The novel developed optimizer called Crisscross Harris Hawks Optimizer (CCHHO), which can effectively achieve high-quality solutions with accelerated convergence on a variety of optimization tasks. In the proposed algorithm, the vertical crossover strategy of CSO is used for adjusting the exploitative ability adaptively to alleviate the local optimum;the horizontal crossover strategy of CSO is considered as an operator for boosting explorative trend;and the competitive operator is adopted to accelerate the convergence rate. The effectiveness of the proposed optimizer is evaluated using 4 kinds of benchmark functions, 3 constrained engineering optimization issues and feature selection problems on 13 datasets from the UCI repository. Comparing with nine conventional intelligence algorithms and 9 state-of-the-art algorithms, the statistical results reveal that the proposed CCHHO is significantly more effective than HHO, CSO, CCNMHHO and other competitors, and its advantage is not influenced by the increase of problems’ dimensions. Additionally, experimental results also illustrate that the proposed CCHHO outperforms some existing optimizers in working out engineering design optimization;for feature selection problems, it is superior to other feature selection methods including CCNMHHO in terms of fitness, error rate and length of selected features. 展开更多
关键词 Harris hawks optimization Bioinspired algorithm Global optimization Engineering optimization Feature selection
基于Harris Hawks优化算法的介质波导滤波器优化设计 认领 引用 被引量：2
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作者 舒佩文 麦健业 褚庆昕 《电波科学学报》 CSCD 北大核心 2021年第5期787-796,共10页
Harris Hawks优化(Harris Hawks optimization, HHO)算法是一种模拟鸟群合作捕食行为的新型群智能算法.介质波导滤波器是当前5G移动通信设备急需的器件,因此如何利用新型优化算法高效且精确地对介质波导滤波器进行优化设计十分重要.文... Harris Hawks优化(Harris Hawks optimization, HHO)算法是一种模拟鸟群合作捕食行为的新型群智能算法.介质波导滤波器是当前5G移动通信设备急需的器件,因此如何利用新型优化算法高效且精确地对介质波导滤波器进行优化设计十分重要.文中首先描述了HHO算法流程,并结合滤波器优化问题提出了一种通用框架;然后基于稳态假设对HHO算法的更新方程进行了理论分析,依据所导出的方程分析了算法的动态特性及收敛行为;最后利用HHO算法实现了两款介质波导滤波器的优化设计.为验证算法性能,将本文算法与三个著名的群智能算法进行比较.实验结果表明,HHO算法的收敛速度、效率和精度都明显优于目前业内主流应用的自适应差分进化算法、花粉授粉优化算法和灰狼优化算法. 展开更多
关键词 群智能优化算法 5G移动通信 Harris Hawks优化(HHO)算法 滤波器优化设计 介质波导滤波器
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基于改进Harris Hawk优化算法的虚拟电厂优化调度研究 认领 引用 被引量：3
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作者 丁君 秦浩庭 +3 位作者 苏鹏 曾雪松 李竞轩 郝巍 《可再生能源》 CAS CSCD 北大核心 2025年第6期829-838,共10页
文章针对虚拟电厂的优化调度问题,提出了一种基于改进Harris Hawk优化算法的调度策略。该策略旨在提高包含光伏、风力发电、燃料电池以及热电联产单元的虚拟电厂的经济性和环境友好性,并引入电动汽车和储能系统分别作为灵活储备和旋转备... 文章针对虚拟电厂的优化调度问题,提出了一种基于改进Harris Hawk优化算法的调度策略。该策略旨在提高包含光伏、风力发电、燃料电池以及热电联产单元的虚拟电厂的经济性和环境友好性,并引入电动汽车和储能系统分别作为灵活储备和旋转备用,建立虚拟电厂灵活性聚合模型,通过改进的Harris Hawk优化算法调度方案。最后进行全面的日前调度和短期调度分析。结果表明,该策略能有效应对可再生能源的不确定性,实现对联络线功率的响应跟随。研究结果为虚拟电厂的协调优化调度提供了新的思路和方法。 展开更多
关键词 虚拟电厂 改进Harris Hawk优化算法 灵活性聚合 日前和短期调度
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基于多级特征提取和IHHO-KELM的变压器油中溶解气体体积分数预测 认领 引用 被引量：3
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作者 傅雨晨 陈星 +3 位作者 付文龙 方念 张凯 曹正江 《高压电器》 CAS CSCD 北大核心 2026年第2期60-70,共11页
油中溶解气体分析是变压器早期故障诊断的主要方法,准确预测未来特征气体体积分数有助于提前获取变压器的运行状态。为此提出了一种基于多级特征提取和IHHO-KELM的变压器油中溶解气体体积分数预测方法。首先,通过自适应白噪声完全集合... 油中溶解气体分析是变压器早期故障诊断的主要方法,准确预测未来特征气体体积分数有助于提前获取变压器的运行状态。为此提出了一种基于多级特征提取和IHHO-KELM的变压器油中溶解气体体积分数预测方法。首先,通过自适应白噪声完全集合经验模态分解将气体体积分数序列分解为多个子序列,利用奇异谱分析对子序列做进一步降噪处理,降低其非平稳性;其次,建立核极限学习机预测模型分别对各子序列进行预测,再将各子序列的预测结果叠加得到油中溶解气体体积分数的最终预测结果,并通过改进哈里斯鹰算法优化其超参数;最后,通过算例验证表明,所提模型具有更优的预测性能,可以更好的追踪油中溶解气体体积分数的变化趋势。 展开更多
关键词 油中溶解气体体积分数预测 自适应白噪声完全集合经验模态分解 奇异谱分析 改进哈里斯鹰算法
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基于能量-熵特征和改进堆叠降噪自编码器的水轮机空化状态识别方法 认领 引用 被引量：2
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作者 刘圳 刘忠 +2 位作者 邹淑云 周泽华 乔帅程 《发电技术》 CSCD 2026年第1期176-184,共9页
【目的】针对混流式水轮机空化声发射(acoustic emission,AE)信号受背景噪声干扰、故障难以识别的问题,提出一种基于能量-熵特征和哈里斯鹰优化(Harris hawks optimization,HHO)算法联合3折交叉验证(3-fold crossvalidation,3Fold)优化... 【目的】针对混流式水轮机空化声发射(acoustic emission,AE)信号受背景噪声干扰、故障难以识别的问题,提出一种基于能量-熵特征和哈里斯鹰优化(Harris hawks optimization,HHO)算法联合3折交叉验证(3-fold crossvalidation,3Fold)优化堆叠降噪自编码器(stacked denoising auto encoder,SDAE)的状态识别方法。【方法】首先,利用变分模态分解算法对信号进行分解,得到一系列固有模态函数。其次,提取相关系数最大的2个固有模态函数的能量和熵特征,构建12维特征向量,输入识别模型。再次,利用HHO算法联合3Fold,对SDAE的超参数进行优化。最后,将HHO-3Fold-SDAE算法与其他算法寻优得到的最优参数分别输入模型中运行,并进行对比分析。【结果】与其他算法相比,HHO-3Fold-SDAE算法具有更小的准确率方差、损失率以及更高的平均准确率;相较于SDAE,其测试集平均准确率提高了6%;相较于HHO-SDAE,其测试集平均准确率提高了4%,准确率方差降低了17%。【结论】所提方法可用于水轮机空化AE信号的分类识别,可为水力机械状态监测提供参考。 展开更多
关键词 水力发电 水轮机 空化状态识别 哈里斯鹰优化(HHO)算法 堆叠降噪自编码器(SDAE) 熵
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基于VMD-WHHO-BLS的无人船位姿预测 认领 引用 被引量：1
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作者 葛泉波 薛子建 +1 位作者 张明川 吴庆涛 《控制理论与应用》 EI CAS CSCD 北大核心 2026年第2期335-347,共13页
随着人工智能技术的发展,在无人控制系统领域中,智能传感器的普及使得各式的无人装备运行数据更加的丰富.水面无人船作为无人智能装备的重要组成部分,其关键环节就在于对其安全稳定的自主控制,因为其结构复杂,并且要长时间在未知的环境... 随着人工智能技术的发展,在无人控制系统领域中,智能传感器的普及使得各式的无人装备运行数据更加的丰富.水面无人船作为无人智能装备的重要组成部分,其关键环节就在于对其安全稳定的自主控制,因为其结构复杂,并且要长时间在未知的环境运作,难免出现各种异常状态,会直接影响无人装备的工作能力,降低其安全性和经济性,所以对无人船的位姿状态进行精确的预测十分必要.本文先利用变分模态分解将时间序列数据分解成若干分量,再采用基于宽度学习系统的方法对无人船中的几类数据进行了预测,同时用基于鲸鱼算法与模拟退火算法改进的哈里斯鹰优化算法对宽度学习中的伪逆求解回归参数进行优化.经仿真实验证明,该方法在预测的准确性和训练速度方面都有一定优势. 展开更多
关键词 无人船 宽度学习系统 位姿预测 变分模态分解 哈里斯鹰优化 鲸鱼群算法
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融合多策略的哈里斯鹰优化算法求解Steiner树问题 认领 引用 被引量：1
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作者 王晓峰 王军霞 +3 位作者 彭庆媛 华盈盈 何飞 唐傲 《郑州大学学报（理学版）》 CAS 北大核心 2026年第3期33-40,共8页
针对传统哈里斯鹰优化算法在解决图的Steiner树问题(Steiner tree problem of graph,GSTP)时存在种群分布不均匀、探索与开发阶段难以平衡以及易陷入局部最优的情况,提出一种融合多策略的哈里斯鹰优化算法。首先,通过S型函数对算法进行... 针对传统哈里斯鹰优化算法在解决图的Steiner树问题(Steiner tree problem of graph,GSTP)时存在种群分布不均匀、探索与开发阶段难以平衡以及易陷入局部最优的情况,提出一种融合多策略的哈里斯鹰优化算法。首先,通过S型函数对算法进行离散化处理,并引入Logistic-Sine混合混沌映射,以优化种群初始化过程。其次,设计了动态自适应权重策略,增强猎物逃逸能量的非线性表达,从而进一步平衡探索与开发行为。最后,在迭代后期对最优个体进行自适应高斯—柯西混合变异扰动,以防止种群过早收敛于局部最优。在多个GSTP实例上进行实验,结果表明,所提算法求解精度更高,收敛速度更快。 展开更多
关键词 Steiner树问题 哈里斯鹰优化算法 Logistic-Sine混合混沌映射 自适应逃逸能量 高斯—柯西变异算子
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基于改进哈里斯鹰优化算法的微电网容量优化配置 认领 引用
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作者 李圣清 吴京航 +1 位作者 高泽华 乔靖潇 《太阳能学报》 EI CAS CSCD 北大核心 2026年第7期802-811,共10页
为增强独立微电网的经济性和可靠性,得到微电网中各配置的最佳配比,进行基于改进哈里斯鹰优化算法的微电网容量优化配置研究。以年均综合成本为目标函数,综合考虑各约束条件和分时电价需求响应理论,构建风光柴储微电网配置模型和运行策... 为增强独立微电网的经济性和可靠性,得到微电网中各配置的最佳配比,进行基于改进哈里斯鹰优化算法的微电网容量优化配置研究。以年均综合成本为目标函数,综合考虑各约束条件和分时电价需求响应理论,构建风光柴储微电网配置模型和运行策略。针对哈里斯鹰优化算法在求解模型时易陷入局部收敛以及精度不足的问题,提出一种融合无限折叠迭代混沌映射(ICMIC)混沌映射、正弦余弦算法、纵横交叉算法和Levy飞行策略的改进哈里斯鹰优化算法。通过非零解测试函数,将改进哈里斯鹰优化算法与哈里斯鹰优化算法、灰狼优化算法和蚁狮优化算法进行测试对比,验证了改进哈里斯鹰优化算法具有更优的收敛性能。最后,选取某地区气象数据和负荷数据进行算例分析,求得年综合成本较其他3种算法分别降低10.71%、17.37%和7.92%,验证了改进哈里斯鹰优化算法的有效性。 展开更多
关键词 改进哈里斯鹰优化算法 微电网 容量优化 综合成本
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基于改进哈里斯鹰算法的加压滴灌管网多目标优化布置 认领 引用
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作者 李连豪 金世哲 +4 位作者 杨小密 韩启彪 秦伟桦 肖亚涛 李栋浩 《灌溉排水学报》 CAS CSCD 2026年第6期92-101,共10页
【目的】在轮灌条件下,对加压滴灌管网的布置方案以及轮灌组的划分进行优化,实现滴灌系统全生命周期经济成本的最优控制。【方法】在轮灌方式下的管网布局问题中,轮灌组划分方式从根本上决定管网中的流量分布,从而影响管网布置、管网设... 【目的】在轮灌条件下,对加压滴灌管网的布置方案以及轮灌组的划分进行优化,实现滴灌系统全生命周期经济成本的最优控制。【方法】在轮灌方式下的管网布局问题中,轮灌组划分方式从根本上决定管网中的流量分布,从而影响管网布置、管网设计和管网总成本。兼顾水压、流速,以及轮灌条件下的约束,构建多目标优化模型,以灌溉系统管道全生命周期成本最小作为经济性目标函数,以富余水头绝对值均值与均方差之和为可靠性目标函数,建立数学模型,采用融合混沌初始化、双混沌扰动及自适应精英选择策略改进哈里斯鹰算法(HHO),并用改进后的哈里斯鹰算法求解滴灌系统中多约束条件下的最优解,得出最优的可靠性目标和经济性目标以及该条件下的轮灌组顺序及管径组合。【结果】以河南某滴灌工程为实例,利用改进后MOHHO求解,通过熵权-Topsis法综合评价,筛选出兼顾经济性与可靠性的最优方案;实例结果显示,改进后MOHHO生成的Pareto(帕累托)最优前沿解集质量优于传统算法,同等灌溉均匀性下经济成本更低;熵权-Topsis法可避免决策的主观偏差。【结论】优化方案较原设计全周期成本降低12.36%,富余水头相关指标降低11.10%,为工程设计提供可靠的技术方案。 展开更多
关键词 农田加压滴灌 轮灌组 改进哈里斯鹰算法 Pareto前沿解 多目标优化
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基于IHHO-TOPSIS的盐穴储气库群注采运行智能优化方法 认领 引用
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作者 侯磊 杨喜良 +3 位作者 刘倩 王敏聪 王欣乐 喻鹏飞 《中国石油大学学报（自然科学版）》 EI CAS CSCD 北大核心 2026年第4期69-80,共12页
针对盐穴储气库注采运行中气量分配依赖人工经验的问题,开展多腔联合注采优化研究,旨在实现安全、经济与高效运行的智能调度。以压缩机能耗最低和溶腔总温升最小为目标,构建注气优化模型;以溶腔压降最小和总产能利用率最高为目标,构建... 针对盐穴储气库注采运行中气量分配依赖人工经验的问题,开展多腔联合注采优化研究,旨在实现安全、经济与高效运行的智能调度。以压缩机能耗最低和溶腔总温升最小为目标,构建注气优化模型;以溶腔压降最小和总产能利用率最高为目标,构建采气优化模型;采用改进哈里斯鹰算法求解,结合优劣解距离法对帕累托解集进行综合评价与决策。结果表明:对优化后的注气工况,压缩机能耗平均降低6.97%,温升平均降幅为5.45%;对优化后的采气工况,压降平均降幅为15.93%,总产能利用率平均增加5.11%;采用所提出的优化方法可达到盐穴储气库群注采运行的安全、经济、高效一体化的优化目标。 展开更多
关键词 盐穴储气库 注采运行优化 改进哈里斯鹰算法 优劣解距离法 多目标优化
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基于改进哈里斯鹰算法的光伏清扫机械臂优化 认领 引用
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作者 唐术锋 于慧 +2 位作者 王鑫 郭晓栋 常宏 《太阳能学报》 EI CAS CSCD 北大核心 2026年第2期140-147,共8页
针对现有光伏组件清扫机械臂在常用工作空间性能不高的问题,提出一种改进哈里斯鹰优化算法对机械臂的结构尺寸进行优化,该算法结合正交交叉算子,使得局部搜索能力变得更强。根据实际发电厂光伏组件安装参数,建立常用工作空间的约束指标... 针对现有光伏组件清扫机械臂在常用工作空间性能不高的问题,提出一种改进哈里斯鹰优化算法对机械臂的结构尺寸进行优化,该算法结合正交交叉算子,使得局部搜索能力变得更强。根据实际发电厂光伏组件安装参数,建立常用工作空间的约束指标,并将常用工作空间的全局性能和结构长度两个指标作为目标函数。仿真试验结果表明,改进后的算法寻优更快,针对提出的两个指标分别提高21.27%和8.72%,相同作业环境下,优化后的机械臂到达目标位置所需时间相对于优化前缩短21.7%,机械臂的灵活性提高,清扫光伏组件的效率提升。 展开更多
关键词 光伏组件 机器人 机械臂 光伏组件清扫机器人 哈里斯鹰优化算法 结构优化 移动机器人
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基于HHO-SVMD的激光超声信号去噪 认领 引用
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作者 毛星 王晓晨 +1 位作者 杨荃 李京栋 《东北大学学报（自然科学版）》 EI CAS CSCD 北大核心 2026年第6期164-172,共9页
在工业现场应用中,激光超声信号回波容易被周围的环境噪声淹没,如何在超声信号处理过程中保留微弱的超声信号,针对这一问题,提出了一种基于哈里斯鹰优化(HHO)算法与逐次变分模态分解(SVMD)相结合的激光超声波信号去噪方法HHO-SVMD.首先... 在工业现场应用中,激光超声信号回波容易被周围的环境噪声淹没,如何在超声信号处理过程中保留微弱的超声信号,针对这一问题,提出了一种基于哈里斯鹰优化(HHO)算法与逐次变分模态分解(SVMD)相结合的激光超声波信号去噪方法HHO-SVMD.首先,采用HHO算法对SVMD的关键参数进行优化,从而避免因人为设定参数而导致的噪声分离不充分问题;其次,在信号重构阶段引入巴氏距离作为判据,准确筛选与激光超声信号高度相关的模态分量,从而获得更加纯净的激光超声信号.仿真和实验结果表明,与其他几种现有的去噪方法相比,所提出的方法在保留有效信号的同时,使去噪后信号具有更高的信噪比及更小的均方根误差. 展开更多
关键词 激光超声 信号去噪 逐次变分模态分解 巴氏距离 哈里斯鹰优化算法
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考虑时频耦合的改进DELM短期光伏功率预测 认领 引用
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作者 王瑞 靳鑫鑫 逯静 《控制工程》 CSCD 北大核心 2026年第3期433-443,共11页
针对光伏功率随机性强等特点造成的光伏功率难以预测的问题,提出了一种基于最优多元变分模态分解(optimal multivariate variational mode decomposition, OMVMD)算法以及多策略改进的哈里斯鹰优化(multi-strategy improved Harris hawk... 针对光伏功率随机性强等特点造成的光伏功率难以预测的问题,提出了一种基于最优多元变分模态分解(optimal multivariate variational mode decomposition, OMVMD)算法以及多策略改进的哈里斯鹰优化(multi-strategy improved Harris hawk optimization, MHHO)算法优化深度极限学习机(deep extreme learning machine, DELM)的光伏功率组合预测方法,简称为POMD模型。首先,通过特征选择确定对原始功率贡献值较大的气象特征,并将排列熵作为适应度函数,采用改进的哈里斯鹰优化算法求解MVMD算法的最优参数;然后,采用OMVMD算法对重要特征与实际功率进行同步分解,提高多通道数据的融合处理能力,得到若干个子序列;最后,利用MHHO算法获取DELM网络输入层的最优权重和偏置,搭建光伏功率预测模型,用特征分量来预测功率分量,以实现同频平稳预测的目标。实验结果表明,在三种天气条件下,POMD模型较其他组合方法而言,预测精度更高,拟合效果更好。 展开更多
关键词 最优多元变分模态分解 改进的哈里斯鹰优化算法 深度极限学习机 功率预测
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