An Improved Harris Hawk Optimization Algorithm for Flexible Job Shop Scheduling Problem

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.

An Improved Harris Hawk Optimization Algorithm

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.

Prediction of flyrock distance induced by mine blasting using a novel Harris Hawks optimization-based multi-layer perceptron neural network

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(R^(2)),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.R^(2) of HHO-MLP is 0.998,which achieved the best performance among all five machine learning(ML) models.

Computing Connected Resolvability of Graphs Using Binary Enhanced Harris Hawks Optimization

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.

基于INSHHO算法的多能源发电系统优化调度

为解决风-光-水-火-抽水蓄能多能源发电系统优化调度问题,建立以系统经济效益最高、污染排放最少和系统弃电量最少为目标的多能源发电系统优化调度模型;通过多目标非支配排序哈里斯鹰优化算法与多目标遗传算法的结合,并采用Skew Tent混沌映射进行种群初始化,从而提出改进的多目标非支配排序哈里斯鹰优化算法,对多能源发电系统优化调度模型进行求解;最后对真实算例进行仿真分析,验证了模型的合理性以及所提算法的优越性。

基于IHHO-HKELM输电线路覆冰预测模型

为了进一步提高输电线路覆冰预测精度,提出一种基于改进哈里斯鹰算法(improved harris hawk optimiza-tion,IHHO)优化混合核极限学习机(hybrid kernel extreme learning machine,HKELM)的输电线路覆冰预测模型。在核极限学习机(KELM)中引入混合核函数,形成HKELM,利用黄金正弦、非线性递减能量指数和高斯随机游走等策略对IHHO算法进行改进;以IHHO算法的优化性能采用其对HKELM的权值向量和核参数进行优化,建立基于IHHO-HKELM的输电线路覆冰预测模型,并通过计算气象因素与覆冰厚度之间的灰色关联度确定覆冰预测模型的输入量。算例分析结果表明,IHHO-HKELM模型预测结果的均方误差、最大误差和平均相对误差分别为0.285、0.860 mm和2.83%,预测效果好于其他模型,将本文覆冰预测模型应用于其他覆冰线路,可获得良好的应用效果并验证模型的优越性和实用性。

基于Harris Hawks优化算法的介质波导滤波器优化设计

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.

基于HHO-FA的PEMFC电堆辨识建模

为解决质子交换膜燃料电池(PEMFC)模型参数难以确定的问题,该文提出一种基于哈里斯鹰算法(HHO)和萤火虫算法(FA)联合的优化算法,即HHO-FA算法,用于PEMFC模型的参数辨识。为提高PEMFC建模精确度,HHO-FA保留HHO中搜索效率和精度较高的全局搜索过程,局部寻优过程结合具有群体寻优特征的FA算法,同时优化负责全局搜索和局部搜索切换的转换因子,加入惯性权重因子,优化算法结构。该文使用燃料电池的商业仿真工具箱Thermolib获取算例数据,并通过与粒子群算法(PSO)、HHO算法、蚁群算法(ACO)和FA算法对比分析,对HHO-FA的PEMFC参数辨识性能进行研究。仿真结果表明,相较于PSO、HHO、ACO和FA,HHO-FA的辨识精确度和收敛效率均最高,证实所提出HHO-FA算法在PEMFC模型参数辨识方面的突出性能。

求解有服务顺序限制的MDMOVRPTW的IHHO算法

针对有服务顺序限制的带时间窗的多需求多目标车辆路径问题(multi-demand and multi-objective vehicle routing problem with time window,MDMOVRPTW),在考虑多种需求由不同车辆按顺序服务等约束条件的同时,构建了最小化配送成本和最大化客户满意度的多目标模型。根据模型的特点设计了改进的哈里斯鹰优化(improved Harris hawks optimization,IHHO)算法,随机地将种群中部分支配解作为父代解,用临时组合算子和4种交叉算子搜索新解。最后,算例测试结果表明,相较于传统的哈里斯鹰优化算法,IHHO算法的求解性能得到了有效改善,各操作算子中交叉算子2的求解效果最好。将IHHO算法用于实例中,求解结果得到了改善,充分验证了IHHO算法的有效性。

Enhancing Cancer Classification through a Hybrid Bio-Inspired Evolutionary Algorithm for Biomarker Gene Selection

In this study,our aim is to address the problem of gene selection by proposing a hybrid bio-inspired evolutionary algorithm that combines Grey Wolf Optimization(GWO)with Harris Hawks Optimization(HHO)for feature selection.Themotivation for utilizingGWOandHHOstems fromtheir bio-inspired nature and their demonstrated success in optimization problems.We aimto leverage the strengths of these algorithms to enhance the effectiveness of feature selection in microarray-based cancer classification.We selected leave-one-out cross-validation(LOOCV)to evaluate the performance of both two widely used classifiers,k-nearest neighbors(KNN)and support vector machine(SVM),on high-dimensional cancer microarray data.The proposed method is extensively tested on six publicly available cancer microarray datasets,and a comprehensive comparison with recently published methods is conducted.Our hybrid algorithm demonstrates its effectiveness in improving classification performance,Surpassing alternative approaches in terms of precision.The outcomes confirm the capability of our method to substantially improve both the precision and efficiency of cancer classification,thereby advancing the development ofmore efficient treatment strategies.The proposed hybridmethod offers a promising solution to the gene selection problem in microarray-based cancer classification.It improves the accuracy and efficiency of cancer diagnosis and treatment,and its superior performance compared to other methods highlights its potential applicability in realworld cancer classification tasks.By harnessing the complementary search mechanisms of GWO and HHO,we leverage their bio-inspired behavior to identify informative genes relevant to cancer diagnosis and treatment.

Optimization of Resource Allocation in Unmanned Aerial Vehicles Based on Swarm Intelligence Algorithms

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.

基于多策略混合改进HHO算法的WSN节点覆盖优化

针对监测区域内无线传感器网络节点部署容易出现分布不均匀、有效覆盖率低等问题,提出一种多策略混合改进哈里斯鹰算法的WSN节点覆盖优化策略。利用Fuch无限折叠混沌初始化、自适应精英个体对立学习、正余弦优化和高斯与拉普拉斯最优解变异策略对标准哈里斯鹰优化算法的性能进行改进。利用改进算法求解WSN节点覆盖优化问题,以监测区域网络覆盖率最大为目标,对节点部署位置寻优。实验结果表明,改进策略能够得到更高的网络覆盖率,减少传感节点冗余,延长网络生存时间。

基于G&L-HHO的光束指向精度优化算法

指向精度是光束控制领域最核心的指标之一,指向精度的高低直接决定了光束控制器性能的优劣。为了解决液晶相控阵受制作工艺限制会造成较大光束偏转角度误差、指向精度下降等问题,本文提出一种基于改进哈里斯鹰(HHO)的光束指向精度优化算法。首先,对影响因素进行分析,选取合理的器件参数。随后,采用哈里斯鹰算法对液晶相控阵进行光束指向精度优化。最后,鉴于哈里斯鹰算法容易陷于局部最优值的难题,本文设计了兼顾全局与局部并优策略的哈里斯鹰算法(G&L-HHO)。在全局优化策略方面,选择柯西分布函数增加初始种群多样性,提高算法的全局搜索效率;在局部优化策略方面,采用自适应余弦加权因子,多方面增强哈里斯鹰算法的寻优性能。仿真实验对比结果表明,本研究提出的G&L-HHO算法将归一化精度误差由100数量级优化为10^(-4)数量级,并大幅降低了归一化精度误差波动幅值。本研究显著提高了光束指向精度,并具有收敛精度高、收敛速度快、鲁棒性强等优点。

基于改进HHO⁃LSTM的滚动轴承故障诊断研究

为了解决滚动轴承故障诊断问题,提出一种将改进哈里斯鹰优化(Harris Hawks Optimization,HHO)算法和长短时记忆(Long Short⁃Term Memory,LSTM)网络相融合的智能诊断模型IHHO⁃LSTM。HHO算法在求解过程中容易陷入局部最优、收敛缓慢,基于这些问题引入Cauchy分布函数和模拟退火(Simulated Annealing,SA)算法,拓展全局搜索的广泛性,避免陷入局部最优。运用改进的HHO算法快速确定LSTM模型的最优超参数值,从而提高时序诊断精度。利用凯斯西储大学滚动轴承实验数据进行故障诊断实验,结果表明,IHHO⁃LSTM模型能够实现对滚动轴承的特征提取和故障诊断,模型准确率高达近97%。

基于HHO-QRNN模型的大坝变形预测方法

为有效利用大坝位移数据集中的真实信息,提高预测模型精准度,缩减建模分析训练时间,提出基于卡尔曼滤波算法、完全噪声辅助聚合经验模态分解和准循环神经网络的大坝位移预测方法。首先,模型采用卡尔曼滤波算法对原始输入数据进行处理,提取行有效信息,消除观测噪声影响;其次,设计一种信号分解算法,从累计位移值提取出趋势项、周期项和随机项数据集,以分离不同诱发因素对于大坝位移量的影响;最后,提出一种基于改进哈里斯鹰算法优化准循环神经网络的位移预测算法,对不同数据集分别采用此算法建模预测,将预测结果对应叠加得到最终预测结果。以某水库大坝的历史位移观测数据集为例,将所提模型与其他传统预测模型进行对比分析,结果表明,该模型预测精度和训练速度等方面均有显著提升,验证了其可行性和先进性。

基于HHO-ELM的光伏阵列故障诊断方法研究

光伏阵列长期暴露在恶劣的环境中,导致光伏组件易发生故障,从而影响光伏阵列的发电效率。在实际运行过程中,光伏阵列除发生单一故障之外,还会出现多类型的复合故障,给故障诊断加大了难度。提出了一种基于哈里斯鹰(HHO)算法优化极限学习机(ELM)的光伏阵列多类型复合故障诊断方法。用HHO算法优化ELM的权值和阈值,建立HHO-ELM故障诊断模型,并与ELM、粒子群优化算法(PSO)-ELM、正余弦优化算法(SCA)-ELM以及鲸鱼优化算法(WOA)-ELM算法进行对比。实验结果表明,对于复合故障类型,HHO-ELM模型具有更高的诊断准确率,提高了光伏阵列复合故障的识别精度。

基于IHHO-PNN的变压器复合故障诊断

为了提高变压器复合故障诊断精度,提出了一种基于改进哈里斯鹰(Improved Harris Hawk Optimization, IHHO)算法优化概率神经网络(Probabilistic Neural Network, PNN)的变压器复合故障诊断方法。采用Tent映射、非线性调整逃逸能量和小孔成像学习策略对哈里斯鹰优化(Harris Hawk Optimization, HHO)算法进行改进,以增强IHHO算法的优化性能,避免算法陷入局部最优。采用IHHO算法对PNN的平滑因子进行优化,建立了基于IHHO-PNN的变压器故障诊断模型。利用实际运行的变压器故障数据进行仿真分析。结果表明,所提出的IHHO-PNN模型在进行变压器故障诊断时出现错误诊断的次数更少,诊断精度更高,变压器故障诊断效果好于其他几种对比模型,验证了该变压器复合故障诊断方法的实用性和有效性。

基于PCA-IHHO-LSSVM的变压器绕组热点温度预测

为保障变压器安全稳定运行,提高变压器绕组热点温度预测精度,提出一种基于主成分分析-改进哈里斯鹰-最小二乘支持向量机(principal component analysis-improved Harris hawks optimization-least squares support vector machine,PCA-IHHO-LSSVM)的变压器绕组热点温度预测方法。对变压器绕组热点温度预测的主要特征量进行主成分分析,有功功率、负载电流和顶层油温三个特征量的累计贡献率超过85%,确定有功功率、负载电流和顶层油温三个主元,对输入量进行指标重构。利用Tent混沌映射和学生分布扰动策略对哈里斯鹰算法进行改进,以提高IHHO算法的收敛精度和优化性能,采用IHHO算法对LSSVM进行参数优化,建立了基于PCA-IHHO-LSSVM的变压器绕组热点温度预测模型。采用变压器监测数据进行仿真分析,并与其他绕组热点温度预测方法进行对比,结果表明,所提PCA-IHHO-LSSVM模型的平均相对误差和均方根误差分别为2.92%和1.77℃,预测精度高于其他方法,验证了所提变压器绕组热点温度预测方法的实用性和优越性。

基于改进HHO算法的螺纹钢丝头中径测量方法

针对现阶段基于图像处理进行螺纹中径测量时算法收敛速度慢、精度低的问题,提出一种基于改进哈里斯鹰优化算法的螺纹钢丝头中径测量方法。首先,采用三次样条插值法进行亚像素级的边缘检测,精确提取出螺纹波峰波谷等参数后再构建出中径适应度函数,最后,将螺旋式更新机制和非线性能量递减策略引入哈里斯鹰优化算法(HHO)来求解中径适应度函数。实验结果表明,改进的哈里斯鹰优化算法稳定性更好、精度更高,其中径测量的标准差比传统HHO算法降低了59.33%,中径测量结果的绝对均值误差比三针测量法降低了5.08%,比HHO算法降低37.78%。

基于HHO-KELM的FBG流量温度复合传感解耦

针对光纤布拉格光栅(Fiber Bragg Grating,FBG)流量温度复合传感耦合干扰严重的问题,以小型探针式FBG流量温度复合传感器为研究对象,提出了基于哈里斯鹰算法优化核极限学习机(Harris Hawks Optimizer algorithm Optimized Kernel Extreme Learning Machine,HHO-KELM)的解耦算法。首先,设计了以空心圆柱悬臂梁为受力载体的小型探针式FBG流量温度复合传感器,揭示了该传感器波长漂移量与流量温度的映射关系;然后,构建实验系统进行了流量温度复合传感实验,分析了流量温度耦合特征;最后,利用哈里斯鹰算法优化核极限学习机,获取核极限学习机的最优正则化系数和核函数参数组合,建立了HHO-KELM算法流量温度解耦模型,解耦后流量在2~30 m^(3)/h内,流量平均误差为0.038 m^(3)/h,均方误差为1.91×10^(-3)m^(3)/h,温度平均误差为0.027℃,均方误差为1.03×10^(-3)℃。为验证解耦效果,将HHO-KELM算法与BP算法、ELM算法的解耦结果进行对比。实验结果表明:HHO-KELM算法具有较好的解耦精度和解耦效率,能够有效降低流量温度耦合干扰,提高了传感器的测量精度和稳定性,可实现流量温度的实时动态解耦。