Optimizing Bucket Elevator Performance through a Blend of Discrete Element Method, Response Surface Methodology, and Firefly Algorithm Approaches

This research introduces a novel approach to enhancing bucket elevator design and operation through the integration of discrete element method(DEM)simulation,design of experiments(DOE),and metaheuristic optimization algorithms.Specifically,the study employs the firefly algorithm(FA),a metaheuristic optimization technique,to optimize bucket elevator parameters for maximizing transport mass and mass flow rate discharge of granular materials under specified working conditions.The experimental methodology involves several key steps:screening experiments to identify significant factors affecting bucket elevator operation,central composite design(CCD)experiments to further explore these factors,and response surface methodology(RSM)to create predictive models for transport mass and mass flow rate discharge.The FA algorithm is then applied to optimize these models,and the results are validated through simulation and empirical experiments.The study validates the optimized parameters through simulation and empirical experiments,comparing results with DEM simulation.The outcomes demonstrate the effectiveness of the FA algorithm in identifying optimal bucket parameters,showcasing less than 10%and 15%deviation for transport mass and mass flow rate discharge,respectively,between predicted and actual values.Overall,this research provides insights into the critical factors influencing bucket elevator operation and offers a systematic methodology for optimizing bucket parameters,contributing to more efficient material handling in various industrial applications.

基于ikPCA-FABAS-KELM的短期风电功率预测

为了增强在短期风电功率预测领域中传统数据驱动机器学习模型的精度,提出基于ikPCA-FABAS-KELM的短期风电功率预测模型.首先,对主成分分析进行改进,提出可逆核主成分分析(ikPCA),在保证数据特征的同时,降低输入数据的复杂度,以提升模型运行速度;其次,引入萤火虫个体吸引策略对天牛须算法(BAS)进行改进,提出FABAS算法;最后,利用FABAS算法对核极限学习机(KELM)的正则化参数C和核参数γ进行寻优,降低人为因素对模型盲目训练的影响,提高模型预测精度.仿真结果显示,提出的预测模型有效提高了传统模型的预测精度.

基于FA-BP神经网络的生姜干燥含水率预测

为探索生姜的干燥特性,并实现生姜干燥的含水率预测,研究了不同干燥温度(50、55、60℃)、干燥风速(1.0、2.0、3.0m/s)、切片长度(30、35、40mm)对生姜干燥时间和干燥速率的影响。结合BP神经网络自适应能力、泛化能力、学习能力强和萤火虫算法(FA)参数少、寻优能力强、收敛速度快等特点,将干燥温度、干燥风速、切片长度和干燥时间作为输入层,隐藏层个数为10,输出层为生姜的含水率,搭建一个拓扑结构为“4-10-1”的FA-BP神经网络模型。研究结果表明:干燥温度、干燥风速、切片长度都是影响生姜含水率的关键因素,增加干燥风速、提高干燥温度和减少切片长度能有效缩短生姜的干燥时间,提高干燥效率。选用萤火虫算法优化BP神经网络的权值和阈值,减少了神经网络的训练时间,提高了精准度,其含水率预测值与试验值之间的决定系数R2=0.999 02,均方根误差RMSE为0.002 99,含水率预测结果准确且迅速,能够为生姜干燥过程中的含水率在线预测提供科学依据。

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。

Shape and Size Optimization of Truss Structures under Frequency Constraints Based on Hybrid Sine Cosine Firefly Algorithm

Shape and size optimization with frequency constraints is a highly nonlinear problem withmixed design variables,non-convex search space,and multiple local optima.Therefore,a hybrid sine cosine firefly algorithm(HSCFA)is proposed to acquire more accurate solutions with less finite element analysis.The full attraction model of firefly algorithm(FA)is analyzed,and the factors that affect its computational efficiency and accuracy are revealed.A modified FA with simplified attraction model and adaptive parameter of sine cosine algorithm(SCA)is proposed to reduce the computational complexity and enhance the convergence rate.Then,the population is classified,and different populations are updated by modified FA and SCA respectively.Besides,the random search strategy based on Lévy flight is adopted to update the stagnant or infeasible solutions to enhance the population diversity.Elitist selection technique is applied to save the promising solutions and further improve the convergence rate.Moreover,the adaptive penalty function is employed to deal with the constraints.Finally,the performance of HSCFA is demonstrated through the numerical examples with nonstructural masses and frequency constraints.The results show that HSCFA is an efficient and competitive tool for shape and size optimization problems with frequency constraints.

Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm

The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies.It has already proved its competence in various optimization prob-lems,but it suffers from slow convergence issues.To improve the convergence performance of FA,a new variant named EFA is proposed.The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and FA.As an application of this algorithm to real-world problems,EFA is also applied to optimize the CR system.CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem.However,it requires optimization to meet specific performance objectives.The results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic algorithm.Statistical results further prove that the proposed algorithm is highly efficient and provides superior results.

A Modified Firefly Optimization Algorithm-Based Fuzzy Packet Scheduler for MANET

In Mobile ad hoc Networks(MANETs),the packet scheduling process is considered the major challenge because of error-prone connectivity among mobile nodes that introduces intolerable delay and insufficient throughput with high packet loss.In this paper,a Modified Firefly Optimization Algorithm improved Fuzzy Scheduler-based Packet Scheduling(MFPA-FSPS)Mechanism is proposed for sustaining Quality of Service(QoS)in the network.This MFPA-FSPS mechanism included a Fuzzy-based priority scheduler by inheriting the merits of the Sugeno Fuzzy inference system that potentially and adaptively estimated packets’priority for guaranteeing optimal network performance.It further used the modified Firefly Optimization Algorithm to optimize the rules uti-lized by the fuzzy inference engine to achieve the potential packet scheduling pro-cess.This adoption of a fuzzy inference engine used dynamic optimization that guaranteed excellent scheduling of the necessitated packets at an appropriate time with minimized waiting time.The statistical validation of the proposed MFPA-FSPS conducted using a one-way Analysis of Variance(ANOVA)test confirmed its predominance over the benchmarked schemes used for investigation.

基于FA-PID算法的电动舵机多非线性系统研究

针对制导炮弹电动舵机常规建模时常将各部分视为线性环节,且采用传统三闭环PID算法无法解决复杂非线性系统的高频响、低超调等问题。本研究充分考虑了舵机系统内部的电机、死区齿隙与LuGre摩擦等非线性环节,搭建了电动舵机的多非线性控制系统,根据舵偏角运动方向的非单一性,设计了电机的正反转逻辑换相判别模块,并在电动舵机的位置环中引入了模糊自适应PID(FA-PID)智能算法。结果表明:采用FA-PID算法相比于传统PID控制对小角度位置响应的延迟时间下降了约0.49%,上升时间下降了约6.91%,峰值时间下降了约39.94%,稳态误差下降了约94.44%,且进一步通过小角度方波/正弦位置跟踪研究同样可以得出该算法的快速性与鲁棒性。

Adaptive Kernel Firefly Algorithm Based Feature Selection and Q-Learner Machine Learning Models in Cloud

CC’s(Cloud Computing)networks are distributed and dynamic as signals appear/disappear or lose significance.MLTs(Machine learning Techniques)train datasets which sometime are inadequate in terms of sample for inferring information.A dynamic strategy,DevMLOps(Development Machine Learning Operations)used in automatic selections and tunings of MLTs result in significant performance differences.But,the scheme has many disadvantages including continuity in training,more samples and training time in feature selections and increased classification execution times.RFEs(Recursive Feature Eliminations)are computationally very expensive in its operations as it traverses through each feature without considering correlations between them.This problem can be overcome by the use of Wrappers as they select better features by accounting for test and train datasets.The aim of this paper is to use DevQLMLOps for automated tuning and selections based on orchestrations and messaging between containers.The proposed AKFA(Adaptive Kernel Firefly Algorithm)is for selecting features for CNM(Cloud Network Monitoring)operations.AKFA methodology is demonstrated using CNSD(Cloud Network Security Dataset)with satisfactory results in the performance metrics like precision,recall,F-measure and accuracy used.

基于FA-ISSA-PPR模型的旋风分离器分离效率预测

旋风分离器是气田开发中常用的气固分离设备,准确预测旋风分离器的分离效率对于指导其结构设计和方法优化具有重要意义。在对数据集进行相关性分析的基础上,采用因子分析(factor analysis, FA)简化变量,降低预测模型的复杂程度,利用改进的樽海鞘群算法(improved salp swarm algorithm, ISSA)对投影寻踪(projection pursuit regression, PPR)的模型参数进行优化,形成FA-ISSA-PPR组合模型。结果表明,利用FA模型,原数据集的10个变量可以简化合并为4个公因子,分别代表尺寸参数、颗粒沉降特性、粒子运行轨迹和等效分割粒径对分离效率的影响;与半经验模型和其余机器学习模型相比,组合模型在预测精度和训练时间上具有一定的优越性,在测试样本上的平均绝对误差(MAE)为0.005 91,R^(2)可达0.995,证明了其在小样本、非线性数据分析上的准确性、鲁棒性和泛化性。

基于FA-BP神经网络的巷道位移预测研究

针对传统位移预测算法求解巷道位移时预测精度不佳且误差大等问题,建立萤火虫算法(FA)优化BP神经网络的预测模型,解决了BP神经网络初始权值和阈值难以确定、预测模型参数局部最优及预测精度不佳等问题。以锦丰金矿30中段巷道为研究对象,利用巷道顶板和两帮的位移监测数据进行预测分析,并采用BP神经网络模型与FA-BP神经网络模型进行比较。研究结果表明:FA-BP神经网络模型的平均相对误差分别为0.15%和0.13%,BP神经网络模型分别为-2.02%和0.87%,说明FA-BP神经网络模型具有更好的预测精度。

基于特征能量和BFAGA算法的含分布式电源配电网单相接地故障区段定位

含分布式电源的线-缆混合新型配电网发生单相接地故障时,故障位置采用传统区段定位方法难以准确定位。对此,提出了基于特征能量和二进制萤火虫遗传算法的方法,对新型配电网进行故障区段定位。首先,通过集合经验模态分解求取特征频段内配电网各馈线首端零序电流分量能量之和,判定故障线路进行故障选线;其次,在实现选线的基础上,根据故障区段两端零序电流幅值与健全区段零序电流幅值存在较大差别的特点,采用相关系数法对开关进行实际编码;最后,通过二进制萤火虫遗传算法求得适应度函数最优解,进而得到故障区段。仿真分析以及算法性能对比结果表明,所提方法求解速度更快,准确性更高,具有一定的容错性。

基于IAFA-BP的门式起重机技术特性权重预测

鉴于现有研究对门式起重机设计过程中功能需求与技术特性之间关系的挖掘尚有欠缺,文中提出了基于改进自适应萤火虫算法优化BP神经网络的门式起重机技术特性权重预测模型。通过分析门式起重机功能需求和技术特性之间的关系,根据功能需求重要度预测技术特性的权重,从而实现门式起重机的产品适应性设计。此外,为了提高门式起重机技术特性预测精度,针对萤火虫算法及BP神经网络存在的缺陷,设计了改进的自适应萤火虫算法,用于优化BP神经网络的权值和阈值,验证该模型的有效性。与传统模型进行对比分析,表明该模型具有更高的预测精度。

基于FA-A*优化算法的实验样品配送机器人控制系统设计

为保证配送机器人能够安全稳定地将实验样品送至指定位置,利用FA-A*优化算法,从硬件和软件两个方面优化设计实验样品配送机器人控制系统;改装配送机器人位姿传感器、数据处理器、电机驱动器和控制器等设备元件,调整系统电路的连接方式,完成硬件系统的优化;采用栅格法搭建配送机器人移动环境模型,通过图像采集、特征提取与特征匹配等环节,识别实验样品配送对象的具体位置;以实验样品当前位置为起点、配送终端位置为终点,利用FA-A*优化算法规划机器人配送路径,结合机器人实时位姿的跟踪结果,计算机器人控制量,最终从位置/速度、平衡、自主搭乘电梯等方面,实现配送机器人的控制功能;通过系统测试实验得出结论:综合静态障碍物和动态障碍物两个实验场景,与传统控制相比,在优化设计系统控制下,配送机器人的位置和速度控制误差分别降低约14 m和0.38 m/s。

Firefly Algorithm in Determining Maximum Load Utilization Point and Its Enhancement through Optimal Placement of FACTS Device

In a Power System, load is the most uncertain and extremely time varying unit. Hence it is important to determine the system’s supreme acceptable loadability limit called maximum loadability point to accommodate the sudden variation of load demand. Nowadays the enhancement of the maximum loadability point is essential to meet the rapid growth of load demand by improvising the system’s load utilization capacity. Flexible AC Transmission system devices (FACTS) with their speed and flexibility will play a key role in enhancing the controllability and power transfer capability of the system. Considering the theme of FACTS devices in the loadability limit enhancement, in this paper maximum loadability limit determination and its enhancement are prepared with the help of swarm intelligence based meta-heuristic Firefly Algorithm(FFA) by finding the optimal loading factor for each load and optimally placing the SVC (Shunt Compensation) and TCSC (Series Compensation) FACTS devices in the system. To illuminate the effectiveness of FACTS devices in the loadability enhancement, the line contingency scenario is also concerned in the study. The study of FACTS based maximum system load utilization acceptability point determination is demonstrated with the help of modified IEEE 30 bus, IEEE 57 Bus and IEEE 118 Bus test systems. The results of FACTS devices involvement in determining the maximum loading point enhance the load utilization point in normal state and also help to overcome the system violation in transmissionline contingency state. Also the firefly algorithm in determining the maximum loadability point provides better search capability with faster convergence rate compared to that of Particle swarm optimization (PSO) and Differential evolution algorithm.

基于DEFA-LSSAR的水利工程边坡力学参数预测模型

为了解决现有水利工程边坡力学参数预测模型准确率偏低的问题,利用最小二乘支持向量机LSSAR对水利工程边坡力学参数(弹性模量E)进行预测,结合改进的萤火虫算法对模型进行优化,提出一种基于DEFA-LSSAR的水利工程边坡力学参数预测模型。将本文所提模型分别与樽海鞘群算法、果蝇算法和哈里斯鹰优化算法优化的LSSAR模型进行对比。分析结果表明,所提出的模型预测准确率最高,达94%以上,且具有最小的适应度值,验证了所提模型的有效性和正确性。

基于CFA方法的工业机器人轴承故障信号诊断分析

为了提高工业机器人薄壁角接触球轴承的故障信号诊断效率,设计了一种混沌优化萤火虫参数(CFA)故障信号诊断方法,建立了其相应的控制流程。以工业机器人轴承测试故障平台为依托,开展振动信号处理分析。研究结果表明:模态分量信号已经实现了合成信号准确分离,达到了理想分离性能,有助于从信号包络谱内提取获得故障信号频率。混沌优化萤火虫方法得到的故障诊断准确率明显增加,接近99%左右。相比较其他方法,混沌优化萤火虫方法准确率是最优的,表现出来很高的准确性。该研究可以拓展到其他的机械传动领域,具有很好的应用价值。

Defect image segmentation using multilevel thresholding based on firefly algorithm with opposition-learning

To segment defects from the quad flat non-lead QFN package surface a multilevel Otsu thresholding method based on the firefly algorithm with opposition-learning is proposed. First the Otsu thresholding algorithm is expanded to a multilevel Otsu thresholding algorithm. Secondly a firefly algorithm with opposition-learning OFA is proposed.In the OFA opposite fireflies are generated to increase the diversity of the fireflies and improve the global search ability. Thirdly the OFA is applied to searching multilevel thresholds for image segmentation. Finally the proposed method is implemented to segment the QFN images with defects and the results are compared with three methods i.e. the exhaustive search method the multilevel Otsu thresholding method based on particle swarm optimization and the multilevel Otsu thresholding method based on the firefly algorithm. Experimental results show that the proposed method can segment QFN surface defects images more efficiently and at a greater speed than that of the other three methods.

Rayleigh wave nonlinear inversion based on the Firefly algorithm

Rayleigh waves have high amplitude, low frequency, and low velocity, which are treated as strong noise to be attenuated in reflected seismic surveys. This study addresses how to identify useful shear wave velocity profile and stratigraphic information from Rayleigh waves. We choose the Firefly algorithm for inversion of surface waves. The Firefly algorithm, a new type of particle swarm optimization, has the advantages of being robust, highly effective, and allows global searching. This algorithm is feasible and has advantages for use in Rayleigh wave inversion with both synthetic models and field data. The results show that the Firefly algorithm, which is a robust and practical method, can achieve nonlinear inversion of surface waves with high resolution.

基于FA-ELM深度挖掘模型的电力工程预算控制技术

针对现有预算控制方法存在目标单一,效果不理想等问题,提出了一种基于FA-ELM深度挖掘模型的电力工程预算控制技术。通过深入剖析电力工程费用的组成与影响因素,提出了工程进度与预算双目标的管控方式。利用萤火虫算法优化极限学习机网络,构建FA-ELM预测模型,将预处理后的电力数据输入FA-ELM模型中,可估计每个阶段的工程费用,便于管理人员采取相应的措施。在MATLAB仿真平台上对所提技术进行实验分析,结果表明:FA-ELM模型的预测误差均控制在6%以内,且工程总费用节约了14.09%,整体性能优于其他对比技术。